Littérature scientifique sur le sujet « Data security and Data privacy »
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Articles de revues sur le sujet "Data security and Data privacy"
Yerbulatov, Sultan. « Data Security and Privacy in Data Engineering ». International Journal of Science and Research (IJSR) 13, no 4 (5 avril 2024) : 232–36. http://dx.doi.org/10.21275/es24318121241.
Texte intégralHennessy, S. D., G. D. Lauer, N. Zunic, B. Gerber et A. C. Nelson. « Data-centric security : Integrating data privacy and data security ». IBM Journal of Research and Development 53, no 2 (mars 2009) : 2:1–2:12. http://dx.doi.org/10.1147/jrd.2009.5429044.
Texte intégralSuleiman, James, et Terry Huston. « Data Privacy and Security ». International Journal of Information Security and Privacy 3, no 2 (avril 2009) : 42–53. http://dx.doi.org/10.4018/jisp.2009040103.
Texte intégralGaff, Brian M., Thomas J. Smedinghoff et Socheth Sor. « Privacy and Data Security ». Computer 45, no 3 (mars 2012) : 8–10. http://dx.doi.org/10.1109/mc.2012.102.
Texte intégralAdam, J. A. « Data security-cryptography=privacy ? » IEEE Spectrum 29, no 8 (août 1992) : 29–35. http://dx.doi.org/10.1109/6.144533.
Texte intégralS, Surya Prasad, et Gobi Natesan. « Ensuring Data Security and Privacy in Cloud Infrastructure ». International Journal of Research Publication and Reviews 5, no 3 (21 mars 2024) : 5012–16. http://dx.doi.org/10.55248/gengpi.5.0324.0817.
Texte intégralKumar.R, Dr Prasanna, Porselvan G, Prem Kumar S et Robinlash F. « Security and Privacy Based Data Sharing in Cloud Computing ». International Journal of Innovative Research in Engineering & ; Management 5, no 1 (janvier 2018) : 42–49. http://dx.doi.org/10.21276/ijirem.2018.5.1.9.
Texte intégralGeorge, Jomin, et Takura Bhila. « Security, Confidentiality and Privacy in Health of Healthcare Data ». International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30 juin 2019) : 373–77. http://dx.doi.org/10.31142/ijtsrd23780.
Texte intégralKapil, Gayatri, Alka Agrawal et R. A. Khan. « Big Data Security and Privacy Issues ». Asian Journal of Computer Science and Technology 7, no 2 (5 août 2018) : 128–32. http://dx.doi.org/10.51983/ajcst-2018.7.2.1861.
Texte intégralDanish, Muhammad. « Big Data Security And Privacy ». International Journal of Computer Trends and Technology 67, no 5 (25 mai 2019) : 20–26. http://dx.doi.org/10.14445/22312803/ijctt-v67i5p104.
Texte intégralThèses sur le sujet "Data security and Data privacy"
DeYoung, Mark E. « Privacy Preserving Network Security Data Analytics ». Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82909.
Texte intégralPh. D.
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.
Texte intégralHuang, Xueli. « Achieving Data Privacy and Security in Cloud ». Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/372805.
Texte intégralPh.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
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.
Texte intégralPeople 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.
Nan, Lihao. « Privacy Preserving Representation Learning For Complex Data ». Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20662.
Texte intégralSmith, Tanshanika Turner. « Examining Data Privacy Breaches in Healthcare ». ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2623.
Texte intégralWernberg, 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.
Texte intégralGholami, 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.
Texte intégral“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
Mai, Guangcan. « Biometric system security and privacy : data reconstruction and template protection ». HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/544.
Texte intégralLiu, Lian. « PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA ». UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.
Texte intégralLivres sur le sujet "Data security and Data privacy"
Salomon, David. Data Privacy and Security. New York, NY : Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9.
Texte intégralP, Kenny J. J., dir. Data privacy and security. Oxford [Oxfordshire] : Pergamon Infotech, 1985.
Trouver le texte intégralRao, Udai Pratap, Sankita J. Patel, Pethuru Raj et Andrea Visconti, dir. Security, Privacy and Data Analytics. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9089-1.
Texte intégralRao, Udai Pratap, Mamoun Alazab, Bhavesh N. Gohil et Pethuru Raj Chelliah, dir. Security, Privacy and Data Analytics. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3569-7.
Texte intégralVaidya, Jaideep. Privacy preserving data mining. New York : Springer, 2006.
Trouver le texte intégralGarcia-Alfaro, Joaquin, Guillermo Navarro-Arribas, Alessandro Aldini, Fabio Martinelli et Neeraj Suri, dir. Data Privacy Management, and Security Assurance. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29883-2.
Texte intégralLivraga, Giovanni, Vicenç Torra, Alessandro Aldini, Fabio Martinelli et Neeraj Suri, dir. Data Privacy Management and Security Assurance. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47072-6.
Texte intégralPrivacy and data security law deskbook. [Frederick, MD] : Aspen Publishers, 2010.
Trouver le texte intégralE, Barnett Denise, et British Computer Society. (Conference), (1995), dir. Patient privacy, confidentiality and data security. Nocton : British Computer Society, 1997.
Trouver le texte intégralE, Ludloff Mary, dir. Privacy and big data. Sebastopol, CA : O'Reilly Media, 2011.
Trouver le texte intégralChapitres de livres sur le sujet "Data security and Data privacy"
Barker, Ken. « “Valuing” Privacy While Exposing Data Utility ». Dans 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.
Texte intégralThuraisngham, Bhavani, Murat Kantarcioglu et Latifur Khan. « Data Security and Privacy ». Dans Secure Data Science, 15–28. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003081845-4.
Texte intégralFraser, Ross. « Data Privacy and Security ». Dans Health Informatics, 267–93. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58740-6_10.
Texte intégralFraser, Ross. « Data Privacy and Security ». Dans Health Informatics, 231–50. London : Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-2999-8_11.
Texte intégralPape, Sebastian. « Privacy and Data Security ». Dans Authentication in Insecure Environments, 213–27. Wiesbaden : Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-07116-5_8.
Texte intégralThuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen et Latifur Khan. « Data Security and Privacy ». Dans 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.
Texte intégralThuraisingham, Bhavani. « Web Security and Privacy ». Dans Data and Application Security, 125–26. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/0-306-47008-x_11.
Texte intégralZhang, Rui. « Acquiring Key Privacy from Data Privacy ». Dans Information Security and Cryptology, 359–72. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21518-6_25.
Texte intégralSalomon, David. « Data Hiding in Text ». Dans 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.
Texte intégralSalomon, David. « Data Hiding in Images ». Dans 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.
Texte intégralActes de conférences sur le sujet "Data security and Data privacy"
Bertino, Elisa. « Big data security and privacy ». Dans 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840581.
Texte intégralChen, Zefeng, Jiayang Wu, Wensheng Gan et Zhenlian Qi. « Metaverse Security and Privacy : An Overview ». Dans 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021112.
Texte intégralShi, Yue. « Data Security and Privacy Protection in Public Cloud ». Dans 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622531.
Texte intégralThuraisingham, Bhavani. « Big Data Security and Privacy ». Dans 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.
Texte intégralBertino, Elisa. « Big Data - Security and Privacy ». Dans 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.126.
Texte intégralJeyakumar, Vimalkumar, Omid Madani, Ali ParandehGheibi et Navindra Yadav. « Data Driven Data Center Network Security ». Dans 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.
Texte intégralNelson, Boel, et Tomas Olovsson. « Security and privacy for big data : A systematic literature review ». Dans 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841037.
Texte intégralZhan, Justin. « Privacy Preserving Collaborative Data Mining ». Dans 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379472.
Texte intégralCuzzocrea, Alfredo. « Privacy and Security of Big Data ». Dans the First International Workshop. New York, New York, USA : ACM Press, 2014. http://dx.doi.org/10.1145/2663715.2669614.
Texte intégralSu, Chunli. « Big Data Security and Privacy Protection ». Dans 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). IEEE, 2019. http://dx.doi.org/10.1109/icvris.2019.00030.
Texte intégralRapports d'organisations sur le sujet "Data security and Data privacy"
Vonk, Jaynie. Going Digital : Privacy and data security under GDPR for quantitative impact evaluation. Oxfam, octobre 2019. http://dx.doi.org/10.21201/2019.5211.
Texte intégralChapman, Sam. PPR2021 - Automated Vehicle Safety Assurance - In-use Safety and Security Monitoring - Task 6 : Data Privacy. TRL, juin 2022. http://dx.doi.org/10.58446/dwll8689.
Texte intégralWarren, David R., Michael A. Bianco, Waheed Nasser, Richard R. Kusman, James Shafer, Jason Venner, Lovell Q. Walls et Samson J. Wright. Agencies Need Improved Financial Data Reporting for Private Security Contractors. Fort Belvoir, VA : Defense Technical Information Center, octobre 2008. http://dx.doi.org/10.21236/ada489769.
Texte intégralEastman, Brittany. Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy Rights. SAE International, juillet 2022. http://dx.doi.org/10.4271/epr2022016.
Texte intégralDukarski, Jennifer. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles. SAE International, septembre 2021. http://dx.doi.org/10.4271/epr2021019.
Texte intégralGuicheney, William, Tinashe Zimani, Hope Kyarisiima et Louisa Tomar. Big Data in the Public Sector : Selected Applications and Lessons Learned. Inter-American Development Bank, octobre 2016. http://dx.doi.org/10.18235/0007024.
Texte intégralLiu, Zhuang, Michael Sockin et Wei Xiong. Data Privacy and Temptation. Cambridge, MA : National Bureau of Economic Research, août 2020. http://dx.doi.org/10.3386/w27653.
Texte intégralZhan, Zhijun, et LiWu Chang. Privacy-Preserving Collaborative Data Mining. Fort Belvoir, VA : Defense Technical Information Center, janvier 2003. http://dx.doi.org/10.21236/ada464602.
Texte intégralHeffetz, Ori, et Katrina Ligett. Privacy and Data-Based Research. Cambridge, MA : National Bureau of Economic Research, septembre 2013. http://dx.doi.org/10.3386/w19433.
Texte intégralLiu, Zhuang, Michael Sockin et Wei Xiong. Data Privacy and Algorithmic Inequality. Cambridge, MA : National Bureau of Economic Research, mai 2023. http://dx.doi.org/10.3386/w31250.
Texte intégral