Relevant bibliographies by topics / Data security and Data privacy

Academic literature on the topic 'Data security and Data privacy'

Author: Grafiati
Published: 1 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Data security and Data privacy":

1

Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

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Hennessy, S. D., G. D. Lauer, N. Zunic, B. Gerber, and A. C. Nelson. "Data-centric security: Integrating data privacy and data security." IBM Journal of Research and Development 53, no. 2 (March 2009): 2:1–2:12. http://dx.doi.org/10.1147/jrd.2009.5429044.

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Suleiman, James, and Terry Huston. "Data Privacy and Security." International Journal of Information Security and Privacy 3, no. 2 (April 2009): 42–53. http://dx.doi.org/10.4018/jisp.2009040103.

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Gaff, Brian M., Thomas J. Smedinghoff, and Socheth Sor. "Privacy and Data Security." Computer 45, no. 3 (March 2012): 8–10. http://dx.doi.org/10.1109/mc.2012.102.

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Adam, J. A. "Data security-cryptography=privacy?" IEEE Spectrum 29, no. 8 (August 1992): 29–35. http://dx.doi.org/10.1109/6.144533.

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S, Surya Prasad, and Gobi Natesan. "Ensuring Data Security and Privacy in Cloud Infrastructure." International Journal of Research Publication and Reviews 5, no. 3 (March 21, 2024): 5012–16. http://dx.doi.org/10.55248/gengpi.5.0324.0817.

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Kumar.R, Dr Prasanna, Porselvan G, Prem Kumar S, and Robinlash F. "Security and Privacy Based Data Sharing in Cloud Computing." International Journal of Innovative Research in Engineering & Management 5, no. 1 (January 2018): 42–49. http://dx.doi.org/10.21276/ijirem.2018.5.1.9.

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George, Jomin, and Takura Bhila. "Security, Confidentiality and Privacy in Health of Healthcare Data." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 373–77. http://dx.doi.org/10.31142/ijtsrd23780.

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Kapil, Gayatri, Alka Agrawal, and R. A. Khan. "Big Data Security and Privacy Issues." Asian Journal of Computer Science and Technology 7, no. 2 (August 5, 2018): 128–32. http://dx.doi.org/10.51983/ajcst-2018.7.2.1861.

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Big data gradually become a hot topic of research and business and has been growing at exponential rate. It is a combination of structured, semi-structured & unstructured data which is generated constantly through various sources from different platforms like web servers, mobile devices, social network, private and public cloud etc. Big data is used in many organisations and enterprises, big data security and privacy have been increasingly concerned. However, there is a clear contradiction between the large data security and privacy and the widespread use of big data. In this paper, we have indicated challenges of security and privacy in big data. Then, we have presented some possible methods and techniques to ensure big data security and privacy.
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Danish, Muhammad. "Big Data Security And Privacy." International Journal of Computer Trends and Technology 67, no. 5 (May 25, 2019): 20–26. http://dx.doi.org/10.14445/22312803/ijctt-v67i5p104.

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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Data security and Data privacy":

1

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.
2

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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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
4

Molema, Karabo Omphile. "The conflict of interest between data sharing and data privacy : a middleware approach." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2415.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.
People who are referred to as data owners in this study, use the Internet for various purposes and one of those is using online services like Gmail, Facebook, Twitter and so on. These online services are offered by organizations which are referred to as data controllers. When data owners use these service provided by data controllers they usually have to agree to the terms and conditions which gives data controllers indemnity against any privacy issues that may be raised by the data owner. Data controllers are then free to share that data with any other organizations, referred to as third parties. Though data controllers are protected from lawsuits it does not necessarily mean they are free of any act that may be considered a privacy violation by the data owner. This thesis aims to arrive at a design proposition using the design science research paradigm for a middleware extension, specifically focused on the Tomcat server which is a servlet engine running on the JVM. The design proposition proposes a client side annotation based API to be used by developers to specify classes which will carry data outside the scope of the data controller's system to a third party system, the specified classes will then have code weaved in that will communicate with a Privacy Engine component that will determine based on data owner's preferences if their data should be shared or not. The output of this study is a privacy enhancing platform that comprises of three components the client side annotation based API used by developers, an extension to Tomcat and finally a Privacy Engine.
5

Nan, Lihao. "Privacy Preserving Representation Learning For Complex Data." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20662.

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Here we consider a common data encryption problem encountered by users who want to disclose some data to gain utility but preserve their private information. Specifically, we consider the inference attack, in which an adversary conducts inference on the disclosed data to gain information about users' private data. Following privacy funnel \cite{makhdoumi2014information}, assuming that the original data $X$ is transformed into $Z$ before disclosing and the log loss is used for both privacy and utility metrics, then the problem can be modeled as finding a mapping $X \rightarrow Z$ that maximizes mutual information between $X$ and $Z$ subject to a constraint that the mutual information between $Z$ and private data $S$ is smaller than a predefined threshold $\epsilon$. In contrast to the original study \cite{makhdoumi2014information}, which only focused on discrete data, we consider the more general and practical setting of continuous and high-dimensional disclosed data (e.g., image data). Most previous work on privacy-preserving representation learning is based on adversarial learning or generative adversarial networks, which has been shown to suffer from the vanishing gradient problem, and it is experimentally difficult to eliminate the relationship with private data $Y$ when $Z$ is constrained to retain more information about $X$. Here we propose a simple but effective variational approach that does not rely on adversarial training. Our experimental results show that our approach is stable and outperforms previous methods in terms of both downstream task accuracy and mutual information estimation.
6

Smith, Tanshanika Turner. "Examining Data Privacy Breaches in Healthcare." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2623.

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Healthcare data can contain sensitive, personal, and confidential information that should remain secure. Despite the efforts to protect patient data, security breaches occur and may result in fraud, identity theft, and other damages. Grounded in the theoretical backdrop of integrated system theory, the purpose of this study was to determine the association between data privacy breaches, data storage locations, business associates, covered entities, and number of individuals affected. Study data consisted of secondary breach information retrieved from the Department of Health and Human Services Office of Civil Rights. Loglinear analytical procedures were used to examine U.S. healthcare breach incidents and to derive a 4-way loglinear model. Loglinear analysis procedures included in the model yielded a significance value of 0.000, p > .05 for the both the likelihood ratio and Pearson chi-square statistics indicating that an association among the variables existed. Results showed that over 70% of breaches involve healthcare providers and revealed that security incidents often consist of electronic or other digital information. Findings revealed that threats are evolving and showed that likely factors other than data loss and theft contribute to security events, unwanted exposure, and breach incidents. Research results may impact social change by providing security professionals with a broader understanding of data breaches required to design and implement more secure and effective information security prevention programs. Healthcare leaders might affect social change by utilizing findings to further the security dialogue needed to minimize security risk factors, protect sensitive healthcare data, and reduce breach mitigation and incident response costs.
7

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.
8

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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Mai, Guangcan. "Biometric system security and privacy: data reconstruction and template protection." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/544.

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Biometric systems are being increasingly used, from daily entertainment to critical applications such as security access and identity management. It is known that biometric systems should meet the stringent requirement of low error rate. In addition, for critical applications, the security and privacy issues of biometric systems are required to be concerned. Otherwise, severe consequence such as the unauthorized access (security) or the exposure of identity-related information (privacy) can be caused. Therefore, it is imperative to study the vulnerability to potential attacks and identify the corresponding risks. Furthermore, the countermeasures should also be devised and patched on the systems. In this thesis, we study the security and privacy issues in biometric systems. We first make an attempt to reconstruct raw biometric data from biometric templates and demonstrate the security and privacy issues caused by the data reconstruction. Then, we make two attempts to protect biometric templates from being reconstructed and improve the state-of-the-art biometric template protection techniques.
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Liu, Lian. "PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.

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Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated. Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks. Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements. Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound.
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Books on the topic "Data security and Data privacy":

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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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P, Kenny J. J., ed. Data privacy and security. Oxford [Oxfordshire]: Pergamon Infotech, 1985.

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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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Rao, Udai Pratap, Mamoun Alazab, Bhavesh N. Gohil, and Pethuru Raj Chelliah, eds. Security, Privacy and Data Analytics. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3569-7.

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

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E, Barnett Denise, and British Computer Society. (Conference), (1995), eds. Patient privacy, confidentiality and data security. Nocton: British Computer Society, 1997.

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Craig, Terry. Privacy and big data. Sebastopol, CA: O'Reilly Media, 2011.

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Book chapters on the topic "Data security and Data privacy":

1

Barker, Ken. "“Valuing” Privacy While Exposing Data Utility." In Data Security and Security Data, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25704-9_1.

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Thuraisngham, Bhavani, Murat Kantarcioglu, and Latifur Khan. "Data Security and Privacy." In Secure Data Science, 15–28. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003081845-4.

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Fraser, Ross. "Data Privacy and Security." In Health Informatics, 267–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58740-6_10.

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Fraser, Ross. "Data Privacy and Security." In Health Informatics, 231–50. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-2999-8_11.

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Pape, Sebastian. "Privacy and Data Security." In Authentication in Insecure Environments, 213–27. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-07116-5_8.

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Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Security and Privacy." In Big Data Analytics with Applications in Insider Threat Detection, 15–26. Boca Raton : Taylor & Francis, CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-3.

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Thuraisingham, Bhavani. "Web Security and Privacy." In Data and Application Security, 125–26. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/0-306-47008-x_11.

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Zhang, Rui. "Acquiring Key Privacy from Data Privacy." In Information Security and Cryptology, 359–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21518-6_25.

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Salomon, David. "Data Hiding in Text." In Data Privacy and Security, 245–67. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9_11.

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Salomon, David. "Data Hiding in Images." In Data Privacy and Security, 269–337. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9_12.

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Conference papers on the topic "Data security and Data privacy":

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Bertino, Elisa. "Big data security and privacy." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840581.

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Chen, Zefeng, Jiayang Wu, Wensheng Gan, and Zhenlian Qi. "Metaverse Security and Privacy: An Overview." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021112.

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Shi, Yue. "Data Security and Privacy Protection in Public Cloud." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622531.

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Thuraisingham, Bhavani. "Big Data Security and Privacy." In CODASPY'15: Fifth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2699026.2699136.

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Bertino, Elisa. "Big Data - Security and Privacy." In 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.126.

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Jeyakumar, Vimalkumar, Omid Madani, Ali ParandehGheibi, and Navindra Yadav. "Data Driven Data Center Network Security." In CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2875475.2875490.

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Nelson, Boel, and Tomas Olovsson. "Security and privacy for big data: A systematic literature review." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841037.

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Zhan, Justin. "Privacy Preserving Collaborative Data Mining." In 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379472.

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Cuzzocrea, Alfredo. "Privacy and Security of Big Data." In the First International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2663715.2669614.

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Su, Chunli. "Big Data Security and Privacy Protection." In 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). IEEE, 2019. http://dx.doi.org/10.1109/icvris.2019.00030.

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Reports on the topic "Data security and Data privacy":

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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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Chapman, Sam. PPR2021 - Automated Vehicle Safety Assurance - In-use Safety and Security Monitoring - Task 6: Data Privacy. TRL, June 2022. http://dx.doi.org/10.58446/dwll8689.

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This report assesses the data privacy considerations associated with proposals for an in-use safety and security monitoring scheme. This work presents the current legal framework of data privacy in the UK as well as internationally. Based on this the proposals for data capture are assessed against the regulatory requirements to identify any issues. This work finds that the benefits of in-use monitoring to ensuring public safety can justify the capture of data provided that privacy concerns are managed appropriately.
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Warren, David R., Michael A. Bianco, Waheed Nasser, Richard R. Kusman, James Shafer, Jason Venner, Lovell Q. Walls, and Samson J. Wright. Agencies Need Improved Financial Data Reporting for Private Security Contractors. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada489769.

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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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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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Guicheney, William, Tinashe Zimani, Hope Kyarisiima, and Louisa Tomar. Big Data in the Public Sector: Selected Applications and Lessons Learned. Inter-American Development Bank, October 2016. http://dx.doi.org/10.18235/0007024.

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This paper analyzes different ways in which big data can be leveraged to improve the efficiency and effectiveness of government. It describes five cases where massive and diverse sets of information are gathered, processed, and analyzed in three different policy areas: smart cities, taxation, and citizen security. The cases, compiled from extensive desk research and interviews with leading academics and practitioners in the field of data analytics, have been analyzed from the perspective of public servants interested in big data and thus address both the technical and the institutional aspects of the initiatives. Based on the case studies, a policy guide was built to orient public servants in Latin America and the Caribbean in the implementation of big data initiatives and the promotion of a data ecosystem. The guide covers aspects such as leadership, governance arrangements, regulatory frameworks, data sharing, and privacy, as well as considerations for storing, processing, analyzing, and interpreting data.
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Liu, Zhuang, Michael Sockin, and Wei Xiong. Data Privacy and Temptation. Cambridge, MA: National Bureau of Economic Research, August 2020. http://dx.doi.org/10.3386/w27653.

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Zhan, Zhijun, and LiWu Chang. Privacy-Preserving Collaborative Data Mining. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada464602.

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Heffetz, Ori, and Katrina Ligett. Privacy and Data-Based Research. Cambridge, MA: National Bureau of Economic Research, September 2013. http://dx.doi.org/10.3386/w19433.

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Liu, Zhuang, Michael Sockin, and Wei Xiong. Data Privacy and Algorithmic Inequality. Cambridge, MA: National Bureau of Economic Research, May 2023. http://dx.doi.org/10.3386/w31250.

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To the bibliography