Academic literature on the topic 'Data Privacy and Security Policies'
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Journal articles on the topic "Data Privacy and Security Policies"
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.
Full textSipes, 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.
Full textChigona, 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.
Full textGao, 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.
Full textO'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.
Full textJoshi, 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.
Full textShetty, 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.
Full textShahid, 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.
Full textFu, 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.
Full textDean, 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.
Full textDissertations / Theses on the topic "Data Privacy and Security Policies"
Scheffler, Thomas. "Privacy enforcement with data owner-defined policies." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6793/.
Full textIm 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.
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.
Full textSalim, 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.
Full textDeYoung, Mark E. "Privacy Preserving Network Security Data Analytics." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82909.
Full textPh. D.
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.
Full textPh.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
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.
Full textChowdhury, 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.
Full textOur 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.
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.
Full textGholami, 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.
Full text“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
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.
Full textBooks on the topic "Data Privacy and Security Policies"
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.
Find full textRiegel, Reinhard. Datenschutz bei den Sicherheitsbehörden: Mit einem Anhang der wichtigsten Fachbegriffe in deutscher, englischer, italienischer und spanischer Sprache. 2nd ed. Köln: Heymann, 1992.
Find full textSloot, 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.
Full textSalomon, David. Data Privacy and Security. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9.
Full textRao, 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.
Full textInstitute, Pennsylvania Bar. Privacy and security. [Mechanicsburg, Pa.] (5080 Ritter Rd., Mechanicsburg 17055-6903): Pennsylvania Bar Institute, 2006.
Find full textVaidya, Jaideep. Privacy preserving data mining. New York: Springer, 2006.
Find full textGarcia-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.
Full textLivraga, 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.
Full textPrivacy and data security law deskbook. [Frederick, MD]: Aspen Publishers, 2010.
Find full textBook chapters on the topic "Data Privacy and Security Policies"
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.
Full textKrumay, 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.
Full textMatteucci, 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.
Full textArdagna, 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.
Full textArdagna, 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.
Full textPatil, 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.
Full textFujita, 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.
Full textPardo, 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.
Full textCuppens, 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.
Full textNeville, 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.
Full textConference papers on the topic "Data Privacy and Security Policies"
"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.
Full textMasoumzadeh, 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.
Full textTomuro, 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.
Full textBatra, 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.
Full textTrabelsi, 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.
Full textBertolissi, 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.
Full textOualha, 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.
Full textTolba, 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.
Full textBecker, 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.
Full textHaddad, 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.
Full textReports on the topic "Data Privacy and Security Policies"
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.
Full textVonk, 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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