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Academic literature on the topic 'Data Privacy and Security Policies'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data Privacy and Security Policies"

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Rosenfeld, Lisa, John Torous, and Ipsit V. Vahia. "Data Security and Privacy in Apps for Dementia: An Analysis of Existing Privacy Policies." American Journal of Geriatric Psychiatry 25, no. 8 (August 2017): 873–77. http://dx.doi.org/10.1016/j.jagp.2017.04.009.

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Sipes, Elizabeth Kemery, Joshua James, and David Zetoony. "Current data security issues for financial services firms." Journal of Investment Compliance 17, no. 3 (September 5, 2016): 55–59. http://dx.doi.org/10.1108/joic-07-2016-0034.

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Purpose To provide a roadmap for financial services firms in designing some key policies and procedures relating to their cybersecurity programs, including document retention policies, creating incident response plans, and starting or evaluating a bounty program. Design/methodology/approach This article is divided into three parts: how to design a document retention policy, how to draft an effective incident response plan, data privacy considerations for starting or evaluating a bounty program. The information is presented in narrative form as well as through a series of practical checklists, questions for consideration and tables to represent data collected from other sources or analyzed by the authors. Findings This article identifies best practices for data security with respect to document retention policies, incident response plans and bounty programs. Originality/value This article includes practical guidance regarding document retention policies, incident response plans and bounty programs from lawyers with experience in data privacy and security, investment management and fund formation. This information is of value to financial services firms, which face potential financial implications and increasing regulatory ramifications, including enforcement actions, fines and penalties, for the failure to adopt tailored cybersecurity programs.
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Chigona, W. "Synchronised smart phones: The collision of personal privacy and organisational data security." South African Journal of Business Management 43, no. 2 (June 29, 2012): 31–40. http://dx.doi.org/10.4102/sajbm.v43i2.181.

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The purpose of this study was to explore the organisational and individual motivations for incorporating personally-owned smart phones into the workplace and challenges arising from use; privacy and data security concerns of involved parties in the organisation. This study uses exploratory case study method and investigates privacy and security regarding personally-owned smart-phone usage in workplace. The study found that convenience, ease of use and access to emails were motives behind employees’ use of personal smart phones in the workplace. Further, employees have higher privacy expectation. Sample for this study was small to provide statistically meaningful results, Further research is needed to cover a larger case study spanning multiple organisations in other sectors. Mobile devices are creating challenges to organisational data security and employees’ right to information privacy. This study suggests that organisations need to reconsider data security and employees’ privacy policies to address possible conflict between data security and employees’ privacy.
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Gao, Lei, and Alisa G. Brink. "A Content Analysis of the Privacy Policies of Cloud Computing Services." Journal of Information Systems 33, no. 3 (July 1, 2018): 93–115. http://dx.doi.org/10.2308/isys-52188.

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ABSTRACT Cloud computing is increasingly popular across all sectors and offers users unparalleled scalability, elasticity, and flexibility. However, the rapid transition toward cloud computing has raised privacy and confidentiality concerns. Cloud service providers can access users' data, and private information may be accidentally or deliberately disclosed or used for unauthorized purposes. Privacy policies are intended to provide users with information about privacy practices and their privacy options. This study performs content analysis of the privacy policies of 47 cloud service providers who offer services to business users. This analysis identifies what information is collected and why, to whom the information is disclosed, and what measures are in place for data security, data retention, and data complaints. Additionally, we investigate the readability, uncertainty language, and linguistic tone of cloud service privacy policies. Our results offer implications for cloud service users, providers, and policymakers, and provide directions for future research. Data Availability: Data are available upon request.
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O'Loughlin, Kristen, Martha Neary, Elizabeth C. Adkins, and Stephen M. Schueller. "Reviewing the data security and privacy policies of mobile apps for depression." Internet Interventions 15 (March 2019): 110–15. http://dx.doi.org/10.1016/j.invent.2018.12.001.

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Joshi, Anupam, Tim Finin, Lalana Kagal, Jim Parker, and Anand Patwardhan. "Security policies and trust in ubiquitous computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1881 (July 31, 2008): 3769–80. http://dx.doi.org/10.1098/rsta.2008.0142.

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Ubiquitous environments comprise resource-constrained mobile and wearable devices and computational elements embedded in everyday artefacts. These are connected to each other using both infrastructure-based as well as short-range ad hoc networks. Limited Internet connectivity limits the use of conventional security mechanisms such as public key infrastructures and other forms of server-centric authentication. Under these circumstances, peer-to-peer interactions are well suited for not just information interchange, but also managing security and privacy. However, practical solutions for protecting mobile devices, preserving privacy, evaluating trust and determining the reliability and accuracy of peer-provided data in such interactions are still in their infancy. Our research is directed towards providing stronger assurances of the reliability and trustworthiness of information and services, and the use of declarative policy-driven approaches to handle the open and dynamic nature of such systems. This paper provides an overview of some of the challenges and issues, and points out directions for progress.
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Shetty, Nisha P., Balachandra Muniyal, and Saleh Mowla. "Policy resolution of shared data in online social networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3767. http://dx.doi.org/10.11591/ijece.v10i4.pp3767-3776.

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Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners.
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Shahid, Arsalan, Thien-An Ngoc Nguyen, and M.-Tahar Kechadi. "Big Data Warehouse for Healthcare-Sensitive Data Applications." Sensors 21, no. 7 (March 28, 2021): 2353. http://dx.doi.org/10.3390/s21072353.

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.
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Fu, Tao. "China’s personal information protection in a data-driven economy: A privacy policy study of Alibaba, Baidu and Tencent." Global Media and Communication 15, no. 2 (May 27, 2019): 195–213. http://dx.doi.org/10.1177/1742766519846644.

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By examining the privacy policies of leading Chinese Internet and information service providers (IISPs), this study found their privacy policies to be generally compliant with China’s personal information protection provisions. These IISPs use proper mechanisms showing their commitment, measures and enforcement to data security, but their Fair Information Practices need improvement. Personal information protection in China is severe. Privacy policies offer more ‘notice’ than they do ‘choice’. Chinese IISPs collect and use information extensively in the guise of providing value to users, but have given insufficient consideration to transborder data flows and change of ownership. Societal and technological mechanisms have not been widely sought.
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Dean, Matthew D., Dinah M. Payne, and Brett J. L. Landry. "Data mining: an ethical baseline for online privacy policies." Journal of Enterprise Information Management 29, no. 4 (July 11, 2016): 482–504. http://dx.doi.org/10.1108/jeim-04-2014-0040.

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Purpose – The purpose of this paper is to advocate for and provide guidance for the development of a code of ethical conduct surrounding online privacy policies, including those concerning data mining. The hope is that this research generates thoughtful discussion on the issue of how to make data mining more effective for the business stakeholder while at the same time making it a process done in an ethical way that remains effective for the consumer. The recognition of the privacy rights of data mining subjects is paramount within this discussion. Design/methodology/approach – The authors derive foundational principles for ethical data mining. First, philosophical literature on moral principles is used as the theoretical foundation. Then, using existing frameworks, including legislation and regulations from a range of jurisdictions, a compilation of foundational principles was derived. This compilation was then evaluated and honed through the integration of stakeholder perspective and the assimilation of moral and philosophical precepts. Evaluating a sample of privacy policies hints that current practice does not meet the proposed principles, indicating a need for changes in the way data mining is performed. Findings – A comprehensive framework for the development a contemporary code of conduct and proposed ethical practices for online data mining was constructed. Research limitations/implications – This paper provides a configuration upon which a code of ethical conduct for performing data mining, tailored to meet the particular needs of any organization, can be designed. Practical implications – The implications of data mining, and a code of ethical conduct regulating it, are far-reaching. Implementation of such principles serve to improve consumer and stakeholder confidence, ensure the enduring compliance of data providers and the integrity of its collectors, and foster confidence in the security of data mining. Originality/value – Existing legal mandates alone are insufficient to properly regulate data mining, therefore supplemental reference to ethical considerations and stakeholder interest is required. The adoption of a functional code of general application is essential to address the increasing proliferation of apprehension regarding online privacy.
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Dissertations / Theses on the topic "Data Privacy and Security Policies"

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Scheffler, Thomas. "Privacy enforcement with data owner-defined policies." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6793/.

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This thesis proposes a privacy protection framework for the controlled distribution and use of personal private data. The framework is based on the idea that privacy policies can be set directly by the data owner and can be automatically enforced against the data user. Data privacy continues to be a very important topic, as our dependency on electronic communication maintains its current growth, and private data is shared between multiple devices, users and locations. The growing amount and the ubiquitous availability of personal private data increases the likelihood of data misuse. Early privacy protection techniques, such as anonymous email and payment systems have focused on data avoidance and anonymous use of services. They did not take into account that data sharing cannot be avoided when people participate in electronic communication scenarios that involve social interactions. This leads to a situation where data is shared widely and uncontrollably and in most cases the data owner has no control over further distribution and use of personal private data. Previous efforts to integrate privacy awareness into data processing workflows have focused on the extension of existing access control frameworks with privacy aware functions or have analysed specific individual problems such as the expressiveness of policy languages. So far, very few implementations of integrated privacy protection mechanisms exist and can be studied to prove their effectiveness for privacy protection. Second level issues that stem from practical application of the implemented mechanisms, such as usability, life-time data management and changes in trustworthiness have received very little attention so far, mainly because they require actual implementations to be studied. Most existing privacy protection schemes silently assume that it is the privilege of the data user to define the contract under which personal private data is released. Such an approach simplifies policy management and policy enforcement for the data user, but leaves the data owner with a binary decision to submit or withhold his or her personal data based on the provided policy. We wanted to empower the data owner to express his or her privacy preferences through privacy policies that follow the so-called Owner-Retained Access Control (ORAC) model. ORAC has been proposed by McCollum, et al. as an alternate access control mechanism that leaves the authority over access decisions by the originator of the data. The data owner is given control over the release policy for his or her personal data, and he or she can set permissions or restrictions according to individually perceived trust values. Such a policy needs to be expressed in a coherent way and must allow the deterministic policy evaluation by different entities. The privacy policy also needs to be communicated from the data owner to the data user, so that it can be enforced. Data and policy are stored together as a Protected Data Object that follows the Sticky Policy paradigm as defined by Mont, et al. and others. We developed a unique policy combination approach that takes usability aspects for the creation and maintenance of policies into consideration. Our privacy policy consists of three parts: A Default Policy provides basic privacy protection if no specific rules have been entered by the data owner. An Owner Policy part allows the customisation of the default policy by the data owner. And a so-called Safety Policy guarantees that the data owner cannot specify disadvantageous policies, which, for example, exclude him or her from further access to the private data. The combined evaluation of these three policy-parts yields the necessary access decision. The automatic enforcement of privacy policies in our protection framework is supported by a reference monitor implementation. We started our work with the development of a client-side protection mechanism that allows the enforcement of data-use restrictions after private data has been released to the data user. The client-side enforcement component for data-use policies is based on a modified Java Security Framework. Privacy policies are translated into corresponding Java permissions that can be automatically enforced by the Java Security Manager. When we later extended our work to implement server-side protection mechanisms, we found several drawbacks for the privacy enforcement through the Java Security Framework. We solved this problem by extending our reference monitor design to use Aspect-Oriented Programming (AOP) and the Java Reflection API to intercept data accesses in existing applications and provide a way to enforce data owner-defined privacy policies for business applications.
Im Rahmen der Dissertation wurde ein Framework für die Durchsetzung von Richtlinien zum Schutz privater Daten geschaffen, welches darauf setzt, dass diese Richtlinien oder Policies direkt von den Eigentümern der Daten erstellt werden und automatisiert durchsetzbar sind. Der Schutz privater Daten ist ein sehr wichtiges Thema im Bereich der elektronischen Kommunikation, welches durch die fortschreitende Gerätevernetzung und die Verfügbarkeit und Nutzung privater Daten in Onlinediensten noch an Bedeutung gewinnt. In der Vergangenheit wurden verschiedene Techniken für den Schutz privater Daten entwickelt: so genannte Privacy Enhancing Technologies. Viele dieser Technologien arbeiten nach dem Prinzip der Datensparsamkeit und der Anonymisierung und stehen damit der modernen Netznutzung in Sozialen Medien entgegen. Das führt zu der Situation, dass private Daten umfassend verteilt und genutzt werden, ohne dass der Datenbesitzer gezielte Kontrolle über die Verteilung und Nutzung seiner privaten Daten ausüben kann. Existierende richtlinienbasiert Datenschutztechniken gehen in der Regel davon aus, dass der Nutzer und nicht der Eigentümer der Daten die Richtlinien für den Umgang mit privaten Daten vorgibt. Dieser Ansatz vereinfacht das Management und die Durchsetzung der Zugriffsbeschränkungen für den Datennutzer, lässt dem Datenbesitzer aber nur die Alternative den Richtlinien des Datennutzers zuzustimmen, oder keine Daten weiterzugeben. Es war daher unser Ansatz die Interessen des Datenbesitzers durch die Möglichkeit der Formulierung eigener Richtlinien zu stärken. Das dabei verwendete Modell zur Zugriffskontrolle wird auch als Owner-Retained Access Control (ORAC) bezeichnet und wurde 1990 von McCollum u.a. formuliert. Das Grundprinzip dieses Modells besteht darin, dass die Autorität über Zugriffsentscheidungen stets beim Urheber der Daten verbleibt. Aus diesem Ansatz ergeben sich zwei Herausforderungen. Zum einen muss der Besitzer der Daten, der Data Owner, in die Lage versetzt werden, aussagekräftige und korrekte Richtlinien für den Umgang mit seinen Daten formulieren zu können. Da es sich dabei um normale Computernutzer handelt, muss davon ausgegangen werden, dass diese Personen auch Fehler bei der Richtlinienerstellung machen. Wir haben dieses Problem dadurch gelöst, dass wir die Datenschutzrichtlinien in drei separate Bereiche mit unterschiedlicher Priorität aufteilen. Der Bereich mit der niedrigsten Priorität definiert grundlegende Schutzeigenschaften. Der Dateneigentümer kann diese Eigenschaften durch eigene Regeln mittlerer Priorität überschrieben. Darüber hinaus sorgt ein Bereich mit Sicherheitsrichtlinien hoher Priorität dafür, dass bestimmte Zugriffsrechte immer gewahrt bleiben. Die zweite Herausforderung besteht in der gezielten Kommunikation der Richtlinien und deren Durchsetzung gegenüber dem Datennutzer (auch als Data User bezeichnet). Um die Richtlinien dem Datennutzer bekannt zu machen, verwenden wir so genannte Sticky Policies. Das bedeutet, dass wir die Richtlinien über eine geeignete Kodierung an die zu schützenden Daten anhängen, so dass jederzeit darauf Bezug genommen werden kann und auch bei der Verteilung der Daten die Datenschutzanforderungen der Besitzer erhalten bleiben. Für die Durchsetzung der Richtlinien auf dem System des Datennutzers haben wir zwei verschiedene Ansätze entwickelt. Wir haben einen so genannten Reference Monitor entwickelt, welcher jeglichen Zugriff auf die privaten Daten kontrolliert und anhand der in der Sticky Policy gespeicherten Regeln entscheidet, ob der Datennutzer den Zugriff auf diese Daten erhält oder nicht. Dieser Reference Monitor wurde zum einen als Client-seitigen Lösung implementiert, die auf dem Sicherheitskonzept der Programmiersprache Java aufsetzt. Zum anderen wurde auch eine Lösung für Server entwickelt, welche mit Hilfe der Aspekt-orientierten Programmierung den Zugriff auf bestimmte Methoden eines Programms kontrollieren kann. In dem Client-seitigen Referenzmonitor werden Privacy Policies in Java Permissions übersetzt und automatisiert durch den Java Security Manager gegenüber beliebigen Applikationen durchgesetzt. Da dieser Ansatz beim Zugriff auf Daten mit anderer Privacy Policy den Neustart der Applikation erfordert, wurde für den Server-seitigen Referenzmonitor ein anderer Ansatz gewählt. Mit Hilfe der Java Reflection API und Methoden der Aspektorientierten Programmierung gelang es Datenzugriffe in existierenden Applikationen abzufangen und erst nach Prüfung der Datenschutzrichtlinie den Zugriff zuzulassen oder zu verbieten. Beide Lösungen wurden auf ihre Leistungsfähigkeit getestet und stellen eine Erweiterung der bisher bekannten Techniken zum Schutz privater Daten dar.
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Salim, Farzad. "Detecting and resolving redundancies in EP3P policies." Thesis, Faculty of Computer Science and Software Engineering, University of Wollongong, 2006. https://eprints.qut.edu.au/28175/1/c28175.pdf.

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Current regulatory requirements on data privacy make it increasingly important for enterprises to be able to verify and audit their compliance with their privacy policies. Traditionally, a privacy policy is written in a natural language. Such policies inherit the potential ambiguity, inconsistency and mis-interpretation of natural text. Hence, formal languages are emerging to allow a precise specification of enforceable privacy policies that can be verified. The EP3P language is one such formal language. An EP3P privacy policy of an enterprise consists of many rules. Given the semantics of the language, there may exist some rules in the ruleset which can never be used, these rules are referred to as redundant rules. Redundancies adversely affect privacy policies in several ways. Firstly, redundant rules reduce the efficiency of operations on privacy policies. Secondly, they may misdirect the policy auditor when determining the outcome of a policy. Therefore, in order to address these deficiencies it is important to identify and resolve redundancies. This thesis introduces the concept of minimal privacy policy - a policy that is free of redundancy. The essential component for maintaining the minimality of privacy policies is to determine the effects of the rules on each other. Hence, redundancy detection and resolution frameworks are proposed. Pair-wise redundancy detection is the central concept in these frameworks and it suggests a pair-wise comparison of the rules in order to detect redundancies. In addition, the thesis introduces a policy management tool that assists policy auditors in performing several operations on an EP3P privacy policy while maintaining its minimality. Formal results comparing alternative notions of redundancy, and how this would affect the tool, are also presented.
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Salim, Farzad. "Detecting and resolving redundancies in EP3P policies." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061120.151611/index.html.

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DeYoung, Mark E. "Privacy Preserving Network Security Data Analytics." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82909.

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The problem of revealing accurate statistics about a population while maintaining privacy of individuals is extensively studied in several related disciplines. Statisticians, information security experts, and computational theory researchers, to name a few, have produced extensive bodies of work regarding privacy preservation. Still the need to improve our ability to control the dissemination of potentially private information is driven home by an incessant rhythm of data breaches, data leaks, and privacy exposure. History has shown that both public and private sector organizations are not immune to loss of control over data due to lax handling, incidental leakage, or adversarial breaches. Prudent organizations should consider the sensitive nature of network security data and network operations performance data recorded as logged events. These logged events often contain data elements that are directly correlated with sensitive information about people and their activities -- often at the same level of detail as sensor data. Privacy preserving data publication has the potential to support reproducibility and exploration of new analytic techniques for network security. Providing sanitized data sets de-couples privacy protection efforts from analytic research. De-coupling privacy protections from analytical capabilities enables specialists to tease out the information and knowledge hidden in high dimensional data, while, at the same time, providing some degree of assurance that people's private information is not exposed unnecessarily. In this research we propose methods that support a risk based approach to privacy preserving data publication for network security data. Our main research objective is the design and implementation of technical methods to support the appropriate release of network security data so it can be utilized to develop new analytic methods in an ethical manner. Our intent is to produce a database which holds network security data representative of a contextualized network and people's interaction with the network mid-points and end-points without the problems of identifiability.
Ph. D.
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Huang, Xueli. "Achieving Data Privacy and Security in Cloud." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/372805.

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Computer and Information Science
Ph.D.
The growing concerns in term of the privacy of data stored in public cloud have restrained the widespread adoption of cloud computing. The traditional method to protect the data privacy is to encrypt data before they are sent to public cloud, but heavy computation is always introduced by this approach, especially for the image and video data, which has much more amount of data than text data. Another way is to take advantage of hybrid cloud by separating the sensitive data from non-sensitive data and storing them in trusted private cloud and un-trusted public cloud respectively. But if we adopt the method directly, all the images and videos containing sensitive data have to be stored in private cloud, which makes this method meaningless. Moreover, the emergence of the Software-Defined Networking (SDN) paradigm, which decouples the control logic from the closed and proprietary implementations of traditional network devices, enables researchers and practitioners to design new innovative network functions and protocols in a much easier, flexible, and more powerful way. The data plane will ask the control plane to update flow rules when the data plane gets new network packets with which it does not know how to deal with, and the control plane will then dynamically deploy and configure flow rules according to the data plane's requests, which makes the whole network could be managed and controlled efficiently. However, this kind of reactive control model could be used by hackers launching Distributed Denial-of-Service (DDoS) attacks by sending large amount of new requests from the data plane to the control plane. For image data, we divide the image is into pieces with equal size to speed up the encryption process, and propose two kinds of method to cut the relationship between the edges. One is to add random noise in each piece, the other is to design a one-to-one mapping function for each piece to map different pixel value into different another one, which cuts off the relationship between pixels as well the edges. Our mapping function is given with a random parameter as inputs to make each piece could randomly choose different mapping. Finally, we shuffle the pieces with another random parameter, which makes the problems recovering the shuffled image to be NP-complete. For video data, we propose two different methods separately for intra frame, I-frame, and inter frame, P-frame, based on their different characteristic. A hybrid selective video encryption scheme for H.264/AVC based on Advanced Encryption Standard (AES) and video data themselves is proposed for I-frame. For each P-slice of P-frame, we only abstract small part of them in private cloud based on the characteristic of intra prediction mode, which efficiently prevents P-frame being decoded. For cloud running with SDN, we propose a framework to keep the controller away from DDoS attack. We first predict the amount of new requests for each switch periodically based on its previous information, and the new requests will be sent to controller if the predicted total amount of new requests is less than the threshold. Otherwise these requests will be directed to the security gate way to check if there is a attack among them. The requests that caused the dramatic decrease of entropy will be filter out by our algorithm, and the rules of these request will be made and sent to controller. The controller will send the rules to each switch to make them direct the flows matching with the rules to honey pot.
Temple University--Theses
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Ma, Jianjie. "Learning from perturbed data for privacy-preserving data mining." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/Summer2006/j%5Fma%5F080406.pdf.

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Chowdhury, Omar Haider. "Formally ensuring the permissibility of obligations in security and privacy policies." Thesis, The University of Texas at San Antonio, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3594559.

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Our society is becoming increasingly dependent on computer information systems for the management of personal information (e.g., medical records, financial data.). Organizations are required to manage and share such information in a manner that conforms to specific privacy regulations (e.g., the Health Insurance Portability and Accountability Act (HIPAA), the Gramm-Leach-Bliley Act (GLBA).). Privacy policies like HIPAA can impose restrictions based on the finite execution history (present requirements) and can also impose future requirements (obligations ). Existing work on checking compliance only investigates whether a certain action respects the present requirements of the policy or investigates whether a certain pending obligation is violated. However, when an obligation is violated they cannot report whether the user was not diligent or whether the policy did not permit the obligation. To this end, we formally specify a property of the policy which we call the Δ-property that statically guarantees that any incurred obligations can be met. When an obligation is violated according to a policy that has the Δ-property, it is safe to assume that the obligation violation is not due to a malformed policy. We prove that checking whether a policy has the Δ-property is undecidable in general. We then develop a sound, semi-automated technique to check whether a policy has the Δ-property under some constraints. We demonstrate the efficacy of our technique by verifying that our interpretation of the HIPAA privacy rule has the Δ-property.

Organizations that intend to be compliant with privacy policies need to rely on their own access control policies to safeguard their resources against unauthorized access. For instance, having access control policy to ensure only valid organization employees have access to the individual's personal information. These access control policies can allow access to a resource provided that the requesting user or some other user promises to perform some obligations. We are particularly interested in user obligations that can depend on and affect the authorization state of the system. Existing work introduces the property "accountability" that ensures that all the incurred user obligations are authorized. However, they assume that obligations cannot further incur other obligations (i.e., no cascading obligations). As a result, it significantly reduces the expressive power of their obligation model as it cannot express several real life scenarios. We show that deciding accountability in the most general case is NP-hard. We then consider several special yet practical cases of cascading obligations and provide a decision procedure for accountability in their presence.

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Wernberg, Max. "Security and Privacy of Controller Pilot Data Link Communication." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156337.

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Newly implemented technologies within the aviation lack, according to recent studies, built in security measures to protect them against outside interference. In this thesis we study the security and privacy status of the digital wireless Controller Pilot Data Link Communication (CPDLC) used in air traffic management alongside other systems to increase the safety and traffic capacity of controlled airspaces. The findings show that CPDCL is currently insecure and exposed to attacks. Any solutions to remedy this must adhere to its low levels of performance. Elliptical Curve Cryptography, Protected ACARS and Host Identity Protocol have been identified as valid solutions to the system’s security drawbacks and all three are possible to implement in the present state of CPDLC.
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Gholami, Ali. "Security and Privacy of Sensitive Data in Cloud Computing." Doctoral thesis, KTH, Parallelldatorcentrum, PDC, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186141.

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Cloud computing offers the prospect of on-demand, elastic computing, provided as a utility service, and it is revolutionizing many domains of computing. Compared with earlier methods of processing data, cloud computing environments provide significant benefits, such as the availability of automated tools to assemble, connect, configure and reconfigure virtualized resources on demand. These make it much easier to meet organizational goals as organizations can easily deploy cloud services. However, the shift in paradigm that accompanies the adoption of cloud computing is increasingly giving rise to security and privacy considerations relating to facets of cloud computing such as multi-tenancy, trust, loss of control and accountability. Consequently, cloud platforms that handle sensitive information are required to deploy technical measures and organizational safeguards to avoid data protection breakdowns that might result in enormous and costly damages. Sensitive information in the context of cloud computing encompasses data from a wide range of different areas and domains. Data concerning health is a typical example of the type of sensitive information handled in cloud computing environments, and it is obvious that most individuals will want information related to their health to be secure. Hence, with the growth of cloud computing in recent times, privacy and data protection requirements have been evolving to protect individuals against surveillance and data disclosure. Some examples of such protective legislation are the EU Data Protection Directive (DPD) and the US Health Insurance Portability and Accountability Act (HIPAA), both of which demand privacy preservation for handling personally identifiable information. There have been great efforts to employ a wide range of mechanisms to enhance the privacy of data and to make cloud platforms more secure. Techniques that have been used include: encryption, trusted platform module, secure multi-party computing, homomorphic encryption, anonymization, container and sandboxing technologies. However, it is still an open problem about how to correctly build usable privacy-preserving cloud systems to handle sensitive data securely due to two research challenges. First, existing privacy and data protection legislation demand strong security, transparency and audibility of data usage. Second, lack of familiarity with a broad range of emerging or existing security solutions to build efficient cloud systems. This dissertation focuses on the design and development of several systems and methodologies for handling sensitive data appropriately in cloud computing environments. The key idea behind the proposed solutions is enforcing the privacy requirements mandated by existing legislation that aims to protect the privacy of individuals in cloud-computing platforms. We begin with an overview of the main concepts from cloud computing, followed by identifying the problems that need to be solved for secure data management in cloud environments. It then continues with a description of background material in addition to reviewing existing security and privacy solutions that are being used in the area of cloud computing. Our first main contribution is a new method for modeling threats to privacy in cloud environments which can be used to identify privacy requirements in accordance with data protection legislation. This method is then used to propose a framework that meets the privacy requirements for handling data in the area of genomics. That is, health data concerning the genome (DNA) of individuals. Our second contribution is a system for preserving privacy when publishing sample availability data. This system is noteworthy because it is capable of cross-linking over multiple datasets. The thesis continues by proposing a system called ScaBIA for privacy-preserving brain image analysis in the cloud. The final section of the dissertation describes a new approach for quantifying and minimizing the risk of operating system kernel exploitation, in addition to the development of a system call interposition reference monitor for Lind - a dual sandbox.
“Cloud computing”, eller “molntjänster” som blivit den vanligaste svenska översättningen, har stor potential. Molntjänster kan tillhandahålla exaktden datakraft som efterfrågas, nästan oavsett hur stor den är; dvs. molntjäns-ter möjliggör vad som brukar kallas för “elastic computing”. Effekterna avmolntjänster är revolutionerande inom många områden av datoranvändning.Jämfört med tidigare metoder för databehandling ger molntjänster mångafördelar; exempelvis tillgänglighet av automatiserade verktyg för att monte-ra, ansluta, konfigurera och re-konfigurera virtuella resurser “allt efter behov”(“on-demand”). Molntjänster gör det med andra ord mycket lättare för or-ganisationer att uppfylla sina målsättningar. Men det paradigmskifte, sominförandet av molntjänster innebär, skapar även säkerhetsproblem och förutsätter noggranna integritetsbedömningar. Hur bevaras det ömsesidiga förtro-endet, hur hanteras ansvarsutkrävandet, vid minskade kontrollmöjligheter tillföljd av delad information? Följaktligen behövs molnplattformar som är såkonstruerade att de kan hantera känslig information. Det krävs tekniska ochorganisatoriska hinder för att minimera risken för dataintrång, dataintrångsom kan resultera i enormt kostsamma skador såväl ekonomiskt som policymässigt. Molntjänster kan innehålla känslig information från många olikaområden och domäner. Hälsodata är ett typiskt exempel på sådan information. Det är uppenbart att de flesta människor vill att data relaterade tillderas hälsa ska vara skyddad. Så den ökade användningen av molntjänster påsenare år har medfört att kraven på integritets- och dataskydd har skärptsför att skydda individer mot övervakning och dataintrång. Exempel på skyd-dande lagstiftning är “EU Data Protection Directive” (DPD) och “US HealthInsurance Portability and Accountability Act” (HIPAA), vilka båda kräverskydd av privatlivet och bevarandet av integritet vid hantering av informa-tion som kan identifiera individer. Det har gjorts stora insatser för att utvecklafler mekanismer för att öka dataintegriteten och därmed göra molntjänsternasäkrare. Exempel på detta är; kryptering, “trusted platform modules”, säker“multi-party computing”, homomorfisk kryptering, anonymisering, container-och “sandlåde”-tekniker.Men hur man korrekt ska skapa användbara, integritetsbevarande moln-tjänster för helt säker behandling av känsliga data är fortfarande i väsentligaavseenden ett olöst problem på grund av två stora forskningsutmaningar. Fördet första: Existerande integritets- och dataskydds-lagar kräver transparensoch noggrann granskning av dataanvändningen. För det andra: Bristande kän-nedom om en rad kommande och redan existerande säkerhetslösningar för att skapa effektiva molntjänster.Denna avhandling fokuserar på utformning och utveckling av system ochmetoder för att hantera känsliga data i molntjänster på lämpligaste sätt.Målet med de framlagda lösningarna är att svara de integritetskrav som ställsi redan gällande lagstiftning, som har som uttalad målsättning att skyddaindividers integritet vid användning av molntjänster.Vi börjar med att ge en överblick av de viktigaste begreppen i molntjäns-ter, för att därefter identifiera problem som behöver lösas för säker databe-handling vid användning av molntjänster. Avhandlingen fortsätter sedan med en beskrivning av bakgrundsmaterial och en sammanfattning av befintligasäkerhets- och integritets-lösningar inom molntjänster.Vårt främsta bidrag är en ny metod för att simulera integritetshot vidanvändning av molntjänster, en metod som kan användas till att identifierade integritetskrav som överensstämmer med gällande dataskyddslagar. Vårmetod används sedan för att föreslå ett ramverk som möter de integritetskravsom ställs för att hantera data inom området “genomik”. Genomik handlari korthet om hälsodata avseende arvsmassan (DNA) hos enskilda individer.Vårt andra större bidrag är ett system för att bevara integriteten vid publice-ring av biologiska provdata. Systemet har fördelen att kunna sammankopplaflera olika uppsättningar med data. Avhandlingen fortsätter med att före-slå och beskriva ett system kallat ScaBIA, ett integritetsbevarande systemför hjärnbildsanalyser processade via molntjänster. Avhandlingens avslutan-de kapitel beskriver ett nytt sätt för kvantifiering och minimering av risk vid“kernel exploitation” (“utnyttjande av kärnan”). Denna nya ansats är ävenett bidrag till utvecklingen av ett nytt system för (Call interposition referencemonitor for Lind - the dual layer sandbox).

QC 20160516

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Kong, Yibing. "Security and privacy model for association databases." Access electronically, 2003. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20031126.142250/index.html.

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Books on the topic "Data Privacy and Security Policies"

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Inc, Aspatore. Recent trends in privacy and data security: Leading lawyers on analyzing information storage regulations and developing effective data protection policies. Boston, Mass.]: Aspatore, 2013.

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Riegel, Reinhard. Datenschutz bei den Sicherheitsbehörden: Mit einem Anhang der wichtigsten Fachbegriffe in deutscher, englischer, italienischer und spanischer Sprache. 2nd ed. Köln: Heymann, 1992.

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Sloot, Bart, Dennis Broeders, and Erik Schrijvers. Exploring the Boundaries of Big Data. NL Amsterdam: Amsterdam University Press, 2016. http://dx.doi.org/10.5117/9789462983588.

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Though the exact nature and delineation of Big Data is still unclear, it seems likely that Big Data will have an enormous impact on our daily lives. Exploring the Bounderies of Big Data serves as preparatory work for The Netherlands Scientific Council for Government Policy's advice to the Dutch government, which has asked the Council to address questions regarding Big Data, security and privacy. It is divided into five parts, each part engaging with a different perspective on Big Data: the technical, empirical, legal, regulatory and international perspective.
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Salomon, David. Data Privacy and Security. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9.

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Rao, Udai Pratap, Sankita J. Patel, Pethuru Raj, and Andrea Visconti, eds. Security, Privacy and Data Analytics. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9089-1.

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Institute, Pennsylvania Bar. Privacy and security. [Mechanicsburg, Pa.] (5080 Ritter Rd., Mechanicsburg 17055-6903): Pennsylvania Bar Institute, 2006.

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Vaidya, Jaideep. Privacy preserving data mining. New York: Springer, 2006.

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Garcia-Alfaro, Joaquin, Guillermo Navarro-Arribas, Alessandro Aldini, Fabio Martinelli, and Neeraj Suri, eds. Data Privacy Management, and Security Assurance. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29883-2.

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Livraga, Giovanni, Vicenç Torra, Alessandro Aldini, Fabio Martinelli, and Neeraj Suri, eds. Data Privacy Management and Security Assurance. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47072-6.

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Privacy and data security law deskbook. [Frederick, MD]: Aspen Publishers, 2010.

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Book chapters on the topic "Data Privacy and Security Policies"

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Dekker, Marnix, Sandro Etalle, and Jerry den Hartog. "Privacy Policies." In Security, Privacy, and Trust in Modern Data Management, 383–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-69861-6_25.

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Krumay, Barbara, and Jennifer Klar. "Readability of Privacy Policies." In Data and Applications Security and Privacy XXXIV, 388–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49669-2_22.

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Matteucci, Ilaria, Paolo Mori, and Marinella Petrocchi. "Prioritized Execution of Privacy Policies." In Data Privacy Management and Autonomous Spontaneous Security, 133–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35890-6_10.

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Ardagna, C. A., E. Damiani, S. De Capitani di Vimercati, and P. Samarati. "Towards Privacy-Enhanced Authorization Policies and Languages." In Data and Applications Security XIX, 16–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535706_2.

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Ardagna, C. A., S. De Capitani di Vimercati, and P. Samarati. "Enhancing User Privacy Through Data Handling Policies." In Data and Applications Security XX, 224–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11805588_16.

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Patil, Vishwas T., and R. K. Shyamasundar. "Undoing of Privacy Policies on Facebook." In Data and Applications Security and Privacy XXXI, 239–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61176-1_13.

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Fujita, Kunihiko, and Yasuyuki Tsukada. "A Notation for Policies Using Feature Structures." In Data Privacy Management and Autonomous Spontaneous Security, 140–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19348-4_11.

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Pardo, Raúl, and Daniel Le Métayer. "Analysis of Privacy Policies to Enhance Informed Consent." In Data and Applications Security and Privacy XXXIII, 177–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22479-0_10.

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Cuppens, Nora, Salaheddine Zerkane, Yanhuang Li, David Espes, Philippe Le Parc, and Frédéric Cuppens. "Firewall Policies Provisioning Through SDN in the Cloud." In Data and Applications Security and Privacy XXXI, 293–310. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61176-1_16.

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Neville, Ultan, and Simon N. Foley. "Reasoning About Firewall Policies Through Refinement and Composition." In Data and Applications Security and Privacy XXX, 268–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41483-6_19.

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Conference papers on the topic "Data Privacy and Security Policies"

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"A Three Layered Model to Implement Data Privacy Policies." In The 5th International Workshop on Security In Information Systems. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002418301550165.

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Masoumzadeh, Amirreza. "Security Analysis of Relationship-Based Access Control Policies." In CODASPY '18: Eighth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3176258.3176323.

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Tomuro, Noriko, Steven Lytinen, and Kurt Hornsburg. "Automatic Summarization of Privacy Policies using Ensemble Learning." In CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2857705.2857741.

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Batra, Gunjan, Vijayalakshmi Atluri, Jaideep Vaidya, and Shamik Sural. "Incremental Maintenance of ABAC Policies." In CODASPY '21: Eleventh ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3422337.3447825.

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Trabelsi, Slim, and Jakub Sendor. "Sticky policies for data control in the cloud." In 2012 Tenth Annual International Conference on Privacy, Security and Trust (PST). IEEE, 2012. http://dx.doi.org/10.1109/pst.2012.6297922.

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Bertolissi, Clara, Maribel Fernandez, and Bhavani Thuraisingham. "Graph-Based Specification of Admin-CBAC Policies." In CODASPY '21: Eleventh ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3422337.3447850.

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Oualha, Nouha. "Reinforcing IoT-Enforced Security Policies." In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 2018. http://dx.doi.org/10.1109/trustcom/bigdatase.2018.00119.

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Tolba, Mouslim, Salem Benferhat, Karim Tabia, and Abdelkader Belkhir. "Handling Capabilities in Security Policies." In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 2018. http://dx.doi.org/10.1109/trustcom/bigdatase.2018.00292.

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Becker, Jorg, Marcel Heddier, Ayten Oksuz, and Ralf Knackstedt. "The Effect of Providing Visualizations in Privacy Policies on Trust in Data Privacy and Security." In 2014 47th Hawaii International Conference on System Sciences (HICSS). IEEE, 2014. http://dx.doi.org/10.1109/hicss.2014.399.

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Haddad, Mehdi, Mohand-Said Hacid, and Robert Laurini. "Data Integration in Presence of Authorization Policies." In 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2012. http://dx.doi.org/10.1109/trustcom.2012.116.

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Reports on the topic "Data Privacy and Security Policies"

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Rao, Jinghai, and Norman Sadeh. Interleaving Semantic Web Reasoning and Service Discovery to Enforce Context-Sensitive Security and Privacy Policies. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada456148.

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Vonk, Jaynie. Going Digital: Privacy and data security under GDPR for quantitative impact evaluation. Oxfam, October 2019. http://dx.doi.org/10.21201/2019.5211.

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Baader, Franz, Francesco Kriegel, Adrian Nuradiansyah, and Rafael Peñaloza. Computing Compliant Anonymisations of Quantified ABoxes w.r.t. EL Policies (Extended Version). Technische Universität Dresden, 2020. http://dx.doi.org/10.25368/2022.263.

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We adapt existing approaches for privacy-preserving publishing of linked data to a setting where the data are given as Description Logic (DL) ABoxes with possibly anonymised (formally: existentially quantified) individuals and the privacy policies are expressed using sets of concepts of the DL EL. We provide a chacterization of compliance of such ABoxes w.r.t. EL policies, and show how optimal compliant anonymisations of ABoxes that are noncompliant can be computed. This work extends previous work on privacypreserving ontology publishing, in which a very restricted form of ABoxes, called instance stores, had been considered, but restricts the attention to compliance. The approach developed here can easily be adapted to the problem of computing optimal repairs of quantified ABoxes.
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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- criminate rather than targeted data collection, because the high volumes of data are processed on an aggregated rather than a personal level and because the policies and decisions based on the statistical correlations found through algorithmic analytics are mostly addressed at large groups or society as a whole rather than specific individuals. This means that large parts of the data-driven environment are currently left unregu- lated and that individuals are often unable to rely on their fundamental rights when addressing the more systemic effects of Big Data processes. This article will discuss how this tension might be relieved by turning to the notion ‘quality of life’, which has the potential of becoming the new standard for the European Court of Human Rights (ECtHR) when dealing with privacy related cases.
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Eastman, Brittany. Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy Rights. SAE International, July 2022. http://dx.doi.org/10.4271/epr2022016.

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Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and addresses some of the legitimate concerns regarding its use by private corporations and government entities, including law enforcement, in public transportation hubs and traffic stops. Constitutional questions, including First, Forth, and Ninth Amendment issues, also remain unanswered. FRS is now a permanent part of transportation technology and society; with meaningful legislation and conscious engineering, it can make future transportation safer and more convenient.
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Goyeneche, Laura, Cynthia Boruchowicz, Florencia Lopez Boo, Luis Tejerina, Benjamin Roseth, and Jennifer Nelson. Pandemics, privacy, and adoption of technology: Perceptions of the use of digital tools and data sharing during COVID-19 from 10 Latin American countries. Inter-American Development Bank, December 2022. http://dx.doi.org/10.18235/0004546.

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This study describes the perception, adoption, and acceptance factors involved in the deployment of digital technologies for public health in Latin America and considers the implications for future digital health interventions. We conducted a descriptive analysis using nationally representative data from a phone survey conducted in 2020 in 10 countries in Latin America. We found that early in the pandemic, in countries with existing applications, 74% of the population used a smartphone, 47% had knowledge of the government app to report symptoms, but only 2% reported using it. Those interviewed reported that they are willing to share their personal data during a pandemic (61%) 50 percentage points higher than in non-pandemic times, although understanding how their personal data was used by the government and private companies was extremely low. More than 70% reported that they would use an application to report symptoms and would use an app that accesses their location or that uses contact tracing technology to alert them about possible exposure. Also, at least half of the users agree with preventive measures against COVID-19 such as daily follow-up calls, tracking via GPS for quarantine enforcement, and daily visits. In all countries, adoption of digital technologies increases if individuals or their relatives report they are infected; it decreases when end-users do not trust the anonymity policies or are concerned about government surveillance. Yet, encouraging greater adoption of digital technologies strongly depends on who designed the technology. Results show that 73% of users would prefer an app designed by an international organization such as the WHO to an app designed by the local government (64%) or a telephone company (56%). The study concludes with a reflection on the promising results of digital technologies and discusses the importance of considering users perceptions, factors for acceptance, and trust when pursuing adoption of digital technologies.
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Dukarski, Jennifer. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles. SAE International, September 2021. http://dx.doi.org/10.4271/epr2021019.

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Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data
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Greenberg, Jane, Samantha Grabus, Florence Hudson, Tim Kraska, Samuel Madden, René Bastón, and Katie Naum. The Northeast Big Data Innovation Hub: "Enabling Seamless Data Sharing in Industry and Academia" Workshop Report. Drexel University, March 2017. http://dx.doi.org/10.17918/d8159v.

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Increasingly, both industry and academia, in fields ranging from biology and social sciences to computing and engineering, are driven by data (Provost & Fawcett, 2013; Wixom, et al, 2014); and both commercial success and academic impact are dependent on having access to data. Many organizations collecting data lack the expertise required to process it (Hazen, et al, 2014), and, thus, pursue data sharing with researchers who can extract more value from data they own. For example, a biosciences company may benefit from a specific analysis technique a researcher has developed. At the same time, researchers are always on the search for real-world data sets to demonstrate the effectiveness of their methods. Unfortunately, many data sharing attempts fail, for reasons ranging from legal restrictions on how data can be used—to privacy policies, different cultural norms, and technological barriers. In fact, many data sharing partnerships that are vital to addressing pressing societal challenges in cities, health, energy, and the environment are not being pursued due to such obstacles. Addressing these data sharing challenges requires open, supportive dialogue across many sectors, including technology, policy, industry, and academia. Further, there is a crucial need for well-defined agreements that can be shared among key stakeholders, including researchers, technologists, legal representatives, and technology transfer officers. The Northeast Big Data Innovation Hub (NEBDIH) took an important step in this area with the recent "Enabling Seamless Data Sharing in Industry and Academia" workshop, held at Drexel University September 29-30, 2016. The workshop brought together representatives from these critical stakeholder communities to launch a national dialogue on challenges and opportunities in this complex space.
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García-Espinosa, J., and C. Soriano. Data management plan. Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.003.

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This document presents the deliverable D8.1 – the Data Management Plan (DMP) of work package 8 of the prodPhD project. It aims to present the plan for the management, generation, collection, security, preservation and sharing of data generated through the prodPhD project. The DMP is a key element for organizing the project’s data. It provides an analysis of the data, which will be collected, processed and published by the prodPhD consortium. The project embraces the initiatives of the European Commission to promote the open access to research data, aiming to improve and maximize access to and reuse of research data generated by Horizon 2020 projects. In this sense prodPhD will adhere to the Open Research Data Pilot (ORD Pilot) fostered by the European Commission, and this DMP will be developed following the standards of data storage, access and management. This plan will detail what data will be generated through the project, whether and how it will be made accessible for the verification and reuse and how it will be curated and preserved. In this context, the term data applies to the information generated during the different experimental campaigns carried out in the project, and specifically to the data, including associated metadata, to be used to validate the computational models and the technical solutions to be developed in the project. This document is the first version of the DMP and may be updated throughout the project, if significant changes (new data, changes in consortium policies, changes in consortium composition, etc.) arise.
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Kira, Beatriz, Rutendo Tavengerwei, and Valary Mumbo. Points à examiner à l'approche des négociations de Phase II de la ZLECAf: enjeux de la politique commerciale numérique dans quatre pays d'Afrique subsaharienne. Digital Pathways at Oxford, March 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/01.

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Abstract:
Realities such as the COVID-19 pandemic have expedited the move to online operations, highlighting the undeniable fact that the world is continuing to go digital. This emphasises the need for policymakers to regulate in a manner that allows them to harness digital trade benefits while also avoiding associated risk. However, given that digital trade remains unco-ordinated globally, with countries adopting different approaches to policy issues, national regulatory divergence on the matter continues, placing limits on the benefits that countries can obtain from digital trade. Given these disparities, ahead of the African Continental Free Trade Area (AfCFTA) Phase II Negotiations, African countries have been considering the best way to harmonise regulations on issues related to digital trade. To do this effectively, AfCFTA members need to identify where divergencies exist in their domestic regulatory systems. This will allow AfCFTA members to determine where harmonisation is possible, as well as what is needed to achieve such harmonisation. This report analyses the domestic regulations and policies of four focus countries – South Africa, Nigeria, Kenya and Senegal – comparing their regulatory approaches to five policy issues: i) regulation of online transactions; ii) cross-border data flows, data localisation, and personal data protection; iii) access to source code and technology transfer; iv) intermediary liability; and v) customs duties on electronic transmissions. The study highlights where divergencies exist in adopted approaches, indicating the need for the four countries – and AfCFTA members in general – to carefully consider the implications of the divergences, and determine where it is possible and beneficial to harmonise approaches. This was intended to encourage AfCFTA member states to take ownership of these issues and reflect on the reforms needed. As seen in Table 1 below, the study shows that the four countries diverge on most of the five policy issues. There are differences in how all four countries regulate online transactions – that is, e-signatures and online consumer protection. Nigeria was the only country out of the four to recognise all types of e-signatures as legally equivalent. Kenya and Senegal only recognise specific e-signatures, which are either issued or validated by a recognised institution, while South Africa adopts a mixed approach, where it recognises all e-signatures as legally valid, but provides higher evidentiary weight to certain types of e-signatures. Only South Africa and Senegal have specific regulations relating to online consumer protection, while Nigeria and Kenya do not have any clear rules. With regards to cross border data flows, data localisation, and personal data protection, the study shows that all four focus countries have regulations that consist of elements borrowed from the European Union (EU) General Data Protection Regulation (GDPR). In particular, this was regarding the need for the data subject's consent, and also the adequacy requirement. Interestingly, the study also shows that South Africa, Kenya and Nigeria also adopt data localisation measures, although at different levels of strictness. South Africa’s data localisation laws are mostly imposed on data that is considered critical – which is then required to be processed within South African borders – while Nigeria requires all data to be processed and stored locally, using local servers. Kenya imposes data localisation measures that are mostly linked to its priority for data privacy. Out of the four focus countries, Senegal is the only country that does not impose any data localisation laws. Although the study shows that all four countries share a position on customs duties on electronic transmissions, it is also interesting to note that none of the four countries currently have domestic regulations or policies on the subject. The report concludes by highlighting that, as the AfCFTA Phase II Negotiations aim to arrive at harmonisation and to improve intra-African trade and international trade, AfCFTA members should reflect on their national policies and domestic regulations to determine where harmonisation is needed, and whether AfCFTA is the right platform for achieving this efficiently.
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