Academic literature on the topic 'Personally-Identifiable information protection'
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Journal articles on the topic "Personally-Identifiable information protection":
Liu, Deliang. "The Protection of Personally Identifiable Information." SCRIPT-ed 4, no. 4 (December 15, 2007): 389–406. http://dx.doi.org/10.2966/scrip.040407.389.
Fugkeaw, Somchart, and Pattavee Sanchol. "Enabling Efficient Personally Identifiable Information Detection with Automatic Consent Discovery." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 17, no. 2 (June 8, 2023): 245–54. http://dx.doi.org/10.37936/ecti-cit.2023172.252270.
Onik, Md Mehedi Hassan, Chul-Soo Kim, Nam-Yong Lee, and Jinhong Yang. "Privacy-aware blockchain for personal data sharing and tracking." Open Computer Science 9, no. 1 (April 15, 2019): 80–91. http://dx.doi.org/10.1515/comp-2019-0005.
Posey, Clay, Uzma Raja, Robert E. Crossler, and A. J. Burns. "Taking stock of organisations’ protection of privacy: categorising and assessing threats to personally identifiable information in the USA." European Journal of Information Systems 26, no. 6 (November 2017): 585–604. http://dx.doi.org/10.1057/s41303-017-0065-y.
Bomba, David, and George Hallit. "Will the new Australian Health Privacy Law provide adequate protection?" Australian Health Review 25, no. 3 (2002): 141. http://dx.doi.org/10.1071/ah020141a.
Mavridis, Ioannis. "Deploying Privacy Improved RBAC in Web Information Systems." International Journal of Information Technologies and Systems Approach 4, no. 2 (July 2011): 70–87. http://dx.doi.org/10.4018/jitsa.2011070105.
Ellis, Donna A. "A case history in architectural acoustics: Security, acoustics, the protection of personally identifiable information (PII), and accessibility for the disabled." Journal of the Acoustical Society of America 136, no. 4 (October 2014): 2182. http://dx.doi.org/10.1121/1.4899907.
Cruz, Bruno Silveira, and Murillo de Oliveira Dias. "Does digital privacy really exist? When the consumer is the product." Asian Journal of Economics and Business Management 1, no. 1 (June 28, 2022): 39–43. http://dx.doi.org/10.53402/ajebm.v1i1.53.
Olabanji, Samuel Oladiipo, Oluseun Babatunde Oladoyinbo, Christopher Uzoma Asonze, Tunbosun Oyewale Oladoyinbo, Samson Abidemi Ajayi, and Oluwaseun Oladeji Olaniyi. "Effect of Adopting AI to Explore Big Data on Personally Identifiable Information (PII) for Financial and Economic Data Transformation." Asian Journal of Economics, Business and Accounting 24, no. 4 (February 26, 2024): 106–25. http://dx.doi.org/10.9734/ajeba/2024/v24i41268.
Georgiadou, Yola, Rolf de By, and Ourania Kounadi. "Location Privacy in the Wake of the GDPR." ISPRS International Journal of Geo-Information 8, no. 3 (March 22, 2019): 157. http://dx.doi.org/10.3390/ijgi8030157.
Dissertations / Theses on the topic "Personally-Identifiable information protection":
Marillonnet, Paul. "La gestion des données personnelles par l'usager au sein des collectivités locales." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS011.
This Ph.D. addresses the user-centric management of Personally Identifiable Information (PII) within local collectivities. It has been realized as part of a CIFRE program between SAMOVAR and Entr’ouvert. There is a strong need to provide the users of the collectivities' online service with some PII management tools for respecting their privacy when submitting online requests to their collectivities. This need is also coupled with the challenges of free software (including open access to the code, and possibility to evaluate the software's security), which is part of Entr’ouvert’s philosophy. For illustration, a realistic use case is identified for the specific context of territorial collectivities and the public administration (TCPA). It enables to establish a list of useful functional requirements, and a set of users capabilities regarding the management of their own PII. The first contribution is about a technical comparative survey of academic and industrial solutions. This survey identifies thirteen solutions belonging to four different categories, and evaluates them according to eighteen functional criteria. Eventually, the survey provides per-category synthesis and identifies an optimal solution for our use case. The second contribution proposes a solution for supporting PII management, which respects the guidelines identified earlier as part of the survey's optimal solution. It also takes into consideration the PII retrieval from third-party sources. The solution, called the PII manager, operates thanks to its three main components: [i] the Source Backend (SB), [ii] the PII Query Interface (PQI) and [iii] the PII Management User Interface (PMUI). A detailed description of each of these three components is given in the manuscript. Additionally, the user-identifier mapping performed by the PQI is identified as a critical part of the solution. It requires security considerations, as failing to verify the consistency of this mapping can enable four types of attacks. The third contribution proposes an identity-matching solution to counteract the previously identified attacks. Indeed, there is a need to verify the validity of user identity information retrieved across several PII sources. This identity-matching solution requires to identify which components of the architecture is involved in that processing, the workflow across these components to support the full processing, and to perform a security analysis of the workflow that proves its strength against identified attempted attacks. The fourth contribution is the software validation of the proposed solutions through a proof of concept. The identity-matching solution is implemented thanks to the Django template filters and Entr’ouvert’s existing User-Relationship Management (URM) tool. The PII manager is also implemented as a new component to the existing software platform. Eventually, new perspectives are drawn. For instance, this research work could benefit from upcoming protocols such as the Grant Negotiation & Authorization Protocol (GNAP). Other new perspectives include the integration of the System for Cross-domain Identity Management (SCIM) into the platform and a larger-scale software validation
Louw, Candice. "Modeling personally identifiable information leakage that occurs through the use of online social networks." Thesis, 2015. http://hdl.handle.net/10210/13846.
With the phenomenal growth of the Online Social Network (OSN) industry in the past few years, users have resorted to storing vast amounts of personal information on these sites. The information stored on these sites is often readily accessible from anywhere in the world and not always protected by adequate security settings. As a result, user information can make its way, unintentionally, into the hands of not only other online users, but also online abusers. Online abusers, better known as cyber criminals, exploit user information to commit acts of identity theft, Advanced Persistent Threats (APTs) and password recovery, to mention only a few. As OSN users are incapable of visualising the process of access to their OSN information, they may choose to never adjust their security settings. This can become synonymous with ultimately setting themselves up to becoming a victim of cyber crime. In this dissertation we aim to address this problem by proposing a prototype system, the Information Deduction Model (IDM) that can visualise and simulate the process of accessing information on an OSN profile. By visually explaining concepts such as information access, deduction and leakage, we aim to provide users with a tool that will enable them to make more informed choices about the security settings on their OSN profiles thereby setting themselves up for a pleasant online experience.
WANG, HSIN-LAN, and 王心嵐. "A Study of De-identification Open Source Tools of Personally Identifiable Information Protection for Small to Medium Sized Online Companies." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/b293w5.
中國文化大學
資訊管理學系碩士在職專班
107
Privacy is often considered as a part of human rights, accepted by the world as a collective value. Nevertheless, the subject is complicated and the boundary often unclear, in particularly subsequent to the uprising internet and digitalization. The digitalization and connectivity of the cyber world indeed have revolutionized the context of the everyday lives of modern societies. Convenience as it may be, it also poses a high risk for privacy breaching, in specific to Personal Identifiable Information. Given this dilemma, regulations and standards are constituted in responding to the call for PII protection. While the regulations become mandatory, organizations who would be collecting/processing, or storing any personal data will now oblige to take measure for compliance with laws. De-identification as one of the measures to PII protection is essential for organizations specifically to those with sensitive data. This thesis will address PII protection issue from the scope of de-identification open source tools. In mapping the standards and regulations from various entities, a set of SOP practice, whereas businesses would then be equipped with directions for laws compliance. The tool recommendations would further enhance enterprises with the ability to PII protection without resulting in third-party services. Sorting through various de-identification tools and filter out the commercial ones, there leave thirteen tools for preview. From there, derives a selected few. which Organizations with different agenda and status quo could find measures that they see fits. Data sharing plays a key role in revolutionizing the world nowadays and will continue to be so. Balancing PII protection with the call for data sharing is the driving force behind this research. Whereas the aim to resolve dilemma of small to medium sized online companies in facing laws constraints is the backbone of this paper. And expectantly, the study could then fill in the gaps between PII protection and data sharing.
Silva, Carlos Jorge Augusto Pereira da. "Detecting and Protecting Personally Identifiable Information through Machine Learning Techniques." Master's thesis, 2020. https://hdl.handle.net/10216/129033.
Silva, Carlos Jorge Augusto Pereira da. "Detecting and Protecting Personally Identifiable Information through Machine Learning Techniques." Dissertação, 2020. https://hdl.handle.net/10216/129033.
Silva, Paulo Miguel Guimarães da. "Contributions to Personal Data Protection and Privacy Preservation in Cloud Environments." Doctoral thesis, 2021. http://hdl.handle.net/10316/95291.
Personal data is currently being used in countless applications in a vast number of areas. Despite national and international legislation, the fact is that individuals still have little to no control over who uses their data and for what purposes. As regulations vary from region to region, data is often stored and processed in multiple locations by multiple data processors. Moreover, the security concerns of a system are sometimes addressed individually or in an ad-hoc manner, which may result in inadequate solutions. In the end, data protection and privacy assurances are still, in many cases, only a theoretical possibility. As such, it is necessary to propose mechanisms that maximise data protection and provide increased privacy assurances. A strategy to ensure appropriate levels of security and privacy is mandatory. In this work, it was possible to design, develop and evaluate mechanisms that fill the issues mentioned above. One of the pillars of this strategy is the inclusion of Authentication, Authorisation and Accounting (AAA) solutions that securely control access to individuals' data. The other pillar relies on the usage of intelligent, automated, and non-intrusive mechanisms that monitor and control personal data to increase privacy assurances. To fulfil such strategy, the development of a cloud-based AAA solution was the very first step to control individuals' access to data. The proposed solution is composed of a reverse proxy, a custom web application and a NoSQL database. The mechanisms proposed in this thesis recur to Natural Language Processing (NLP), Named Entity Recognition (NER) and Machine Learning (ML) algorithms in a hybrid approach. A series of NER models capable of identifying personal information are also trained with algorithms such as Multi-Layer Perceptron (MLP) and Random Forests (RF), using only publicly available datasets as a source of training and validation data. The mechanisms proposed in this work comply with existing regulations and are designed under appropriate cloud-based deployment and life cycle management strategies. Moreover, this thesis proposes a fuzzy privacy risk model that allows the assessment of privacy risk levels associated with data transactions. The advantages and drawbacks of the proposed mechanisms were evaluated in pilot use cases in the scope of two international projects: H2020 EUBra-BIGSEA and H2020 PoSeID-on. The evaluation conducted on both technical and user-centred scenarios indicates that the proposed mechanisms have high data classifying accuracy, support large volumes of data with distinct characteristics and to increase individuals' privacy awareness and control.
Os dados pessoais são atualmente utilizados em inúmeras aplicações num grande número de áreas. Apesar da legislação nacional e internacional, o facto é que indivíduos ainda têm pouco ou nenhum controlo sobre quem usa os seus dados pessoais, e para que fins. Como os regulamentos variam de região para região, os dados geralmente são armazenados e processados em vários locais, e por vários processadores de dados. Além disso, as questões de segurança dos sistemas por vezes são tratadas individualmente ou de maneira ad-hoc, o que pode resultar em soluções inadequadas. No final, a proteção de dados e as garantias de privacidade ainda são, em muitos casos, apenas uma possibilidade teórica. Como tal, é necessário propor mecanismos que maximizem a proteção de dados e forneçam maiores garantias de privacidade. Uma estratégia para garantir níveis adequados de segurança e privacidade é obrigatória. Neste trabalho, foi possível projetar, desenvolver e avaliar mecanismos que atendem às questões mencionadas acima. Um dos pilares desta estratégia é a inclusão de soluções de Autenticação, Autorização e Auditabilidade (AAA) que controlam o acesso aos dados pessoais com segurança. O outro pilar depende do uso de mecanismos inteligentes, automatizados e não intrusivos que monitoram e controlam os dados pessoais de modo a aumentar as garantias de privacidade. Para seguir essa estratégia, o primeiro passo foi o desenvolvimento de uma solução AAA baseada na nuvem, que controla o acesso a dados pessoais. A solução proposta é composta por um procurador reverso, uma aplicação web personalizada e uma base de dados NoSQL. Os mecanismos propostos nesta tese recorrem a Processamento de Linguagem Natural (PNL), Reconhecimento de Entidades Mencionadas (REM) e Aprendizagem Automática (AA) de uma forma híbrida. Uma série de modelos REM capazes de identificar informações pessoais também são treinados com algoritmos tais como Perceptron Multicamada (PM) e Florestas de Decisão Aleatórias (FDA), usando apenas conjuntos de dados publicamente disponíveis, como fonte de dados de treino e validação. Os mecanismos propostos neste trabalho estão em conformidade com os regulamentos existentes e são projetados de acordo com uma implementação baseada em nuvem e estratégias de gestão de ciclo de vida apropriadas. Além disso, esta tese propõe um modelo fuzzy de risco de privacidade que permite avaliar os níveis de risco de privacidade associados às transações de dados. As vantagens e desvantagens dos mecanismos propostos foram avaliadas em casos de uso piloto no âmbito de dois projetos internacionais: H2020 EUBra-BIGSEA e H2020 PoSeID-on. A avaliação realizada em cenários técnicos e centrados no usuário indica que os mecanismos propostos têm alta precisão de classificação de dados, suportam grandes volumes de dados com características distintas e aumentam a perceção e o controle da privacidade dos indivíduos.
Books on the topic "Personally-Identifiable information protection":
United States. Department of Homeland Security. Office of Inspector General. Letter report: DHS's implementation of protective measures for personally identifiable information. Washington, DC: U.S. Dept. of Homeland Security, Office of Inspector General, 2007.
United States. Department of Homeland Security. Office of Inspector General. Better administration of Automated Targeting System controls can further protect personally identifiable information (redacted). Washington, D.C: U.S. Dept. of Homeland Security, Office of Inspector General, 2007.
United States. Congress. House. A bill to regulate the use by interactive computer services of personally identifiable information provided by subscribers to such services. Washington, D.C: U.S. G.P.O., 1999.
United States. Congress. House. A bill to regulate the use by interactive computer services of Social Security account numbers and related personally identifiable information. Washington, D.C: U.S. G.P.O., 1999.
Ione, Auston, and National Library of Medicine (U.S.). Reference Section, eds. Confidentiality of electronic health data: Methods for protecting personally identifiable information : January 1990 through March 1996 : 448 selected citations. Bethesda, Md: U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Library of Medicine, Reference Section, 1996.
Catalano, MaryAnne. Data Breaches of Personally Identifiable Information at Federal Agencies: Analyses and Lessons. Nova Science Publishers, Incorporated, 2014.
Confidentiality of electronic health data: Methods for protecting personally identifiable information : January 1990 through March 1996 : 448 selected citations. Bethesda, Md: U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Library of Medicine, Reference Section, 1996.
Book chapters on the topic "Personally-Identifiable information protection":
Ni, Anna Ya. "Protection of Personally Identifiable Information in Government." In Routledge Handbook on Information Technology in Government, 266–83. Routledge, 2017. http://dx.doi.org/10.4324/9781315683645-17.
White, Garry L., Francis A. Méndez Mediavilla, and Jaymeen R. Shah. "Information Privacy." In Privacy Solutions and Security Frameworks in Information Protection, 52–69. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2050-6.ch004.
Gibson, Neal, and Greg Holland. "A Dual-Database Trusted Broker System for Resolving, Protecting, and Utilizing Multi-Sourced Data." In Information Quality and Governance for Business Intelligence, 352–62. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4892-0.ch018.
Throne, Robin, and Michalina Hendon. "Belmont 2.0." In Methodologies and Ethics for Social Sciences Research, 1–19. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1726-6.ch001.
Erwin, Geoff, and Mike Moncrieff. "Investing in Online Privacy Policy for Small Business as Part of B2C Web Site Management." In Information Communication Technologies, 2998–3006. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-949-6.ch209.
Mavridis, Ioannis. "Deploying Privacy Improved RBAC in Web Information Systems." In Systems Approach Applications for Developments in Information Technology, 298–315. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1562-5.ch020.
Iglezakis, Ioannis. "Personal Data Protection in Digital Libraries." In E-Publishing and Digital Libraries, 413–29. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-031-0.ch019.
Rouse, Timothy, David N. Levine, Allison Itami, and Benjamin Taylor. "Benefit Plan Cybersecurity Considerations." In The Disruptive Impact of FinTech on Retirement Systems, 86–103. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198845553.003.0006.
Throne, Robin, Michalina Hendon, and James Kozinski. "Graduate Student Investigator." In Advances in Educational Technologies and Instructional Design, 1–15. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8646-7.ch001.
Sodiya, Adesina S., and Adegbuyi B. "A Framework for Protecting Users' Privacy in Cloud." In Cyber Law, Privacy, and Security, 479–90. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8897-9.ch023.
Conference papers on the topic "Personally-Identifiable information protection":
Al-Zaben, Nasr, Md Mehedi Hassan Onik, Jinhong Yang, Nam-Yong Lee, and Chul-Soo Kim. "General Data Protection Regulation Complied Blockchain Architecture for Personally Identifiable Information Management." In 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, 2018. http://dx.doi.org/10.1109/iccecome.2018.8658586.
Rathnayake, Ayodhya Prabhashini. "Patient Information and Electronic Health Records: A Legal Appraisal with Reference to European Health Data Space." In SLIIT International Conference on Advancements in Sciences and Humanities 2023. Faculty of Humanities and Sciences, SLIIT, 2023. http://dx.doi.org/10.54389/isyz8327.
Alnemari, Asma, Rajendra K. Raj, Carol J. Romanowski, and Sumita Mishra. "Protecting Personally Identifiable Information (PII) in Critical Infrastructure Data Using Differential Privacy." In 2019 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 2019. http://dx.doi.org/10.1109/hst47167.2019.9032942.
Yang, Jianliang, Xiya Zhang, Kai Liang, and Yuenan Liu. "Exploring the Application of Large Language Models in Detecting and Protecting Personally Identifiable Information in Archival Data: A Comprehensive Study*." In 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. http://dx.doi.org/10.1109/bigdata59044.2023.10386949.
Reports on the topic "Personally-Identifiable information protection":
McCallister, E., T. Grance, and K. A. Scarfone. Guide to protecting the confidentiality of Personally Identifiable Information (PII). Gaithersburg, MD: National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.sp.800-122.