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Статті в журналах з теми "Cloud data protection"
Begum, Shameena, V. Ratna Vasuki, and K. V. V. Srinivas K.V.V.Srinivas. "Data Security and Protection in Cloud Computing." International Journal of Scientific Research 1, no. 2 (June 1, 2012): 31–34. http://dx.doi.org/10.15373/22778179/jul2012/9.
Повний текст джерелаAl-Museelem, Waleed, and Chun Lin Li. "Data Security and Data Privacy in Cloud Computing." Advanced Materials Research 905 (April 2014): 687–92. http://dx.doi.org/10.4028/www.scientific.net/amr.905.687.
Повний текст джерелаG, Indira, Sujitha S, and S. Ganapathy Subramanian. "Data Integrity Protection in Cloud." International Journal on Cybernetics & Informatics 10, no. 2 (May 31, 2021): 211–18. http://dx.doi.org/10.5121/ijci.2021.100223.
Повний текст джерелаHenze, Martin, René Hummen, Roman Matzutt, Daniel Catrein, and Klaus Wehrle. "Maintaining User Control While Storing and Processing Sensor Data in the Cloud." International Journal of Grid and High Performance Computing 5, no. 4 (October 2013): 97–112. http://dx.doi.org/10.4018/ijghpc.2013100107.
Повний текст джерелаSong, Dawn, Elaine Shi, Ian Fischer, and Umesh Shankar. "Cloud Data Protection for the Masses." Computer 45, no. 1 (January 2012): 39–45. http://dx.doi.org/10.1109/mc.2012.1.
Повний текст джерелаSingh, Niharika, and Ashutosh Kumar Singh. "Data Privacy Protection Mechanisms in Cloud." Data Science and Engineering 3, no. 1 (November 25, 2017): 24–39. http://dx.doi.org/10.1007/s41019-017-0046-0.
Повний текст джерелаDai, Xue Bing, Zhao Jing Wang, and Yan Zhang. "Data Security and Privacy Protection of Cloud Computing." Advanced Materials Research 846-847 (November 2013): 1570–73. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1570.
Повний текст джерелаWei, PengCheng, Dahu Wang, Yu Zhao, Sumarga Kumar Sah Tyagi, and Neeraj Kumar. "Blockchain data-based cloud data integrity protection mechanism." Future Generation Computer Systems 102 (January 2020): 902–11. http://dx.doi.org/10.1016/j.future.2019.09.028.
Повний текст джерелаBeckham, Olly, Gord Oldman, Julie Karrie, and Dorth Craig. "Techniques used to formulate confidential data by means of fragmentation and hybrid encryption." International research journal of management, IT and social sciences 6, no. 6 (October 15, 2019): 68–86. http://dx.doi.org/10.21744/irjmis.v6n6.766.
Повний текст джерелаM. Dillibabu, M. Dillibabu, S. Kumari S. Kumari, T. Saranya T. Saranya, and R. Preethi R. Preethi. "Assured Protection & Veracity for Cloud Data Using Merkle Hash Tree Algorithm." Indian Journal of Applied Research 3, no. 3 (October 1, 2011): 124–26. http://dx.doi.org/10.15373/2249555x/mar2013/39.
Повний текст джерелаДисертації з теми "Cloud data protection"
Oduyiga, Adeshola Oyesanya. "Security in Cloud Storage : A Suitable Security Algorithm for Data Protection." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34428.
Повний текст джерелаSyckor, Jens. "Dropbox & Co, alles schon ge-cloud?" Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-153998.
Повний текст джерелаSobati, Moghadam Somayeh. "Contributions to Data Privacy in Cloud Data Warehouses." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2020.
Повний текст джерелаNowadays, data outsourcing scenarios are ever more common with the advent of cloud computing. Cloud computing appeals businesses and organizations because of a wide variety of benefits such as cost savings and service benefits. Moreover, cloud computing provides higher availability, scalability, and more effective disaster recovery rather than in-house operations. One of the most notable cloud outsourcing services is database outsourcing (Database-as-a-Service), where individuals and organizations outsource data storage and management to a Cloud Service Provider (CSP). Naturally, such services allow storing a data warehouse (DW) on a remote, untrusted CSP and running on-line analytical processing (OLAP).Although cloud data outsourcing induces many benefits, it also brings out security and in particular privacy concerns. A typical solution to preserve data privacy is encrypting data locally before sending them to an external server. Secure database management systems use various encryption schemes, but they either induce computational and storage overhead or reveal some information about data, which jeopardizes privacy.In this thesis, we propose a new secure secret splitting scheme (S4) inspired by Shamir’s secret sharing. S4 implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 addresses the shortcomings of existing approaches by reducing storage and computational overhead while still enforcing a reasonable level of privacy. S4 is efficient both in terms of storage and computing, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources. Experimental results confirm the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.Moreover, we also present new order-preserving schemes, order-preserving indexing (OPI) and wrap-around order-preserving indexing (waOPI), which are practical on cloud outsourced DWs. We focus on the problem of performing range and exact match queries over encrypted data. In contrast to existing solutions, our schemes prevent performing statistical and frequency analysis by an adversary. While providing data privacy, the proposed schemes bear good performance and lead to minimal change for existing software
Skolmen, Dayne Edward. "Protection of personal information in the South African cloud computing environment: a framework for cloud computing adoption." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/12747.
Повний текст джерелаXu, Cheng. "Authenticated query processing in the cloud." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/620.
Повний текст джерелаCerf, Sophie. "control theory for computing systems : application to big-data cloud services & location privacy protection." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT024.
Повний текст джерелаThis thesis presents an application of Control Theory for Computing Systems. It aims at investigating techniques to build and control efficient, dependable and privacy-preserving computing systems. Ad-hoc service configuration require a high level of expertise which could benefit from automation in many ways. A control algorithm can handle bigger and more complex systems, even when they are extremely sensitive to variations in their environment. However, applying control to computing systems raises several challenges, e.g. no physics governs the applications. On one hand, the mathematical framework provided by control theory can be used to improve automation and robustness of computing systems. Moreover, the control theory provides by definition mathematical guarantees that its objectives will be fulfilled. On the other hand, the specific challenges of such use cases enable to expand the control theory itself. The approach taken in this work is to use two application computing systems: location privacy and cloud control. Those two use-cases are complementary in the nature of their technologies and softwares, their scale and in their end-users.The widespread of mobile devices has fostered the broadcasting and collection of users’ location data. It could be for the user to benefit from a personalized service (e.g. weather forecast or route planning) or for the service provider or any other third party to derive useful information from the mobility databases (e.g. road usage frequency or popularity of places). Indeed, many information can be retrieved from location data, including highly sensitive personal data. To overcome this privacy breach, Location Privacy Protection Mechanisms (LPPMs) have been developed. They are algorithm that modify the user’s mobility data, hopefully to hide some sensitive information. However, those tools are not easily configurable by non experts and are static processes that do not adapt to the user’s mobility. We develop two tools, one for already collected databases and one for online usage, that, by tuning the LPPMs, guarantee to the users objective-driven levels of privacy protection and of service utility preservation. First, we present an automated tool able to choose and configure LPPMs to protect already collected databases while ensuring a trade-off between privacy protection and database processing quality. Second, we present the first formulation of the location privacy challenge in control theory terms (plant and control, disturbance and performance signals), and a feedback controller to serve as a proof of concept. In both cases, design, implementation and validation has been done through experiments using data of real users collected on the field.The surge in data generation of the last decades, the so-called bigdata, has lead to the development of frameworks able to analyze them, such as the well known MapReduce. Advances in computing practices has also settled the cloud paradigms (where low-level resources can be rented to allow the development of higher level application without dealing with consideration such as investment in hardware or maintenance) as premium solution for all kind of users. Ensuring the performances of MapReduce jobs running on clouds is thus a major concern for the big IT companies and their clients. In this work, we develop advanced monitoring techniques of the jobs execution time and the platform availability by tuning the resource cluster size and realizing admission control, in spite of the unpredictable client workload. In order to deal with the non linearities of the MapReduce system, a robust adaptive feedback controller has been designed. To reduce the cluster utilization (leading to massive financial and energetic costs), we present a new event-based triggering mechanism formulation combined with an optimal predictive controller. Evaluation is done on a MapReduce benchmark suite running on a large-scale cluster, and using real jobs workloads
Van, der Schyff Karl Izak. "Cloud information security : a higher education perspective." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1011607.
Повний текст джерелаVillarino, Marzo Jorge. "La privacidad en el entorno del cloud computing." Doctoral thesis, Universitat Abat Oliba, 2017. http://hdl.handle.net/10803/456904.
Повний текст джерелаLa evolución tecnológica ha tenido un enorme impacto en los derechos fundamentales, dando lugar al nacimiento de la cuarta generación de derechos. Uno de estos derechos ha sido, sin duda, el derecho a la protección de datos. La privacidad constituye una de las grandes preocupaciones de la sociedad. Por esta razón, cualquier desarrollo tecnológico plantea nuevos retos a la regulación de la protección de datos La computación en nube es una nueva realidad tecnológica caracterizada por la ubicuidad, la elasticidad, el dinamismo, la virtualización, la escalabilidad y el pago bajo demanda. En este trabajo se analiza si la regulación actual del derecho fundamental a la protección de datos es válida para hacer frente a los retos que plantea la computación en nube o si es necesario un nuevo régimen jurídico
The technological evolution has had a great impact on fundamental rights, giving rise to the fourth generation of human rights. One of these has been, with no doubts, the right to data protection. Privacy is one of the main concerns of society. For this reason, any new technological development poses new challenges to data protection regulation. Cloud computing is a new technological reality characterized by ubiquity, elasticity, dynamism, virtualization, scalability and pay on demand. In this dissertation we will analyze if the current data protection regulation is valid to face the new challenges pose by cloud computing or if a new legal regime is mandatory.
Imine, Youcef. "Cloud computing security." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2520.
Повний текст джерелаThese last years, we are witnessing a real digital revolution of Internet where many innovative applications such as Internet of Things, autonomous cars, etc., have emerged. Consequently, adopting externalization technologies such as cloud and fog computing to handle this technological expansion seems to be an inevitable outcome. However, using the cloud or fog computing as a data repository opens many challenges in prospect. This thesis addresses security issues in cloud and fog computing which is a major challenge that need to be appropriately overcomed. Indeed, adopting these technologies means that the users lose control over their own data, which exposes it to several security threats. Therefore, we first investigated the main security issues facing the adoption of cloud and fog computing technologies. As one of the main challenges pointed in our investigation, access control is indeed a cornerstone of data security. An efficient access control mechanism must provide enforced and flexible access policies that ensure data protection, even from the service provider. Hence, we proposed a novel secure and efficient attribute based access control scheme for cloud data-storage applications. Our solution ensures flexible and fine-grained access control and prevents security degradations. Moreover, it performs immediate users and attributes revocation without any key regeneration. Authentication service in fog computing architecture is another issue that we have addressed in this thesis. Some traditional authentication schemes endure latency issues while others do not satisfy fog computing requirements such as mutual authentication between end-devices and fog servers. Thus, we have proposed a new, secure and efficient authentication scheme that ensures mutual authentication at the edge of the network and remedies to fog servers' misbehaviors.Finally, we tackled accountability and privacy-preserving challenges in information-sharing applications for which several proposals in the literature have treated privacy issues, but few of them have considered accountability service. Therefore, we have proposed a novel accountable privacy preserving solution for public information sharing in data externalization platforms. Externalization servers in our scheme authenticate any user in the system without violating its privacy. In case of misbehavior, our solution allows to trace malicious users thanks to an authority
Trebulová, Debora. "Zálohování dat a datová úložiště." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2017. http://www.nusl.cz/ntk/nusl-318599.
Повний текст джерелаКниги з теми "Cloud data protection"
Data Security Council of India. Data protection challenges in cloud computing: An Indian perspective : study report. New Delhi: Data Security Council of India, 2010.
Знайти повний текст джерелаBrennscheidt, Kirstin. Cloud Computing und Datenschutz. Baden-Baden: Nomos, 2013.
Знайти повний текст джерелаCheung, Anne S. Y., and Rolf H. Weber. Privacy and legal issues in cloud computing. Cheltenham, UK ; Northampton, MA, USA: Edward Elgar Publishing., 2015.
Знайти повний текст джерелаKalteis, Michael. Neue Technologien und netzbasierte Medien als Herausforderungen des Datenschutzrechts: Untersuchungen am Beispiel von Cloud Computing, Smart Metering und dem Einsatz mobiler Apps. Wien: Verlag Österreich, 2014.
Знайти повний текст джерелаAuditing cloud computing: A security and privacy guide. Hoboken, N.J: John Wiley & Sons, 2011.
Знайти повний текст джерелаUnited States. Congress. House. Committee on Homeland Security. Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies. Cloud computing: What are the security implications? : hearing before the Subcommittee on Cybersecurity, Infrastructure Protection, and Security Technologies of the Committee on Homeland Security, House of Representatives, One Hundred Twelfth Congress, first session, October 6, 2011. Washington: U.S. Government Printing Office, 2012.
Знайти повний текст джерелаCloud computing: An overview of the technology and the issues facing American innovators : hearing before the Subcommittee on Intellectual Property, Competition, and the Internet of the Committee on the Judiciary, House of Representatives, One Hundred Twelfth Congress, second session, July 25, 2012. Washington: U.S. G.P.O., 2012.
Знайти повний текст джерелаErnesto, Pimentel, Zavattaro Gianluigi, and SpringerLink (Online service), eds. Service-Oriented and Cloud Computing: First European Conference, ESOCC 2012, Bertinoro, Italy, September 19-21, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Знайти повний текст джерелаUnited, States Congress House Committee on the Judiciary Subcommittee on Crime Terrorism Homeland Security and Investigations. ECPA: Lawful access to stored content : hearing before the Subcommittee on Crime, Terrorism, Homeland Security, and Investigations of the Committee on the Judiciary, House of Representatives, One Hundred Thirteenth Congress, first session, March 19, 2013. Washington: U.S. Government Printing Office, 2013.
Знайти повний текст джерелаCappellini, Vito, ed. Electronic Imaging & the Visual Arts. EVA 2014 Florence. Florence: Firenze University Press, 2014. http://dx.doi.org/10.36253/978-88-6655-573-5.
Повний текст джерелаЧастини книг з теми "Cloud data protection"
De Capitani di Vimercati, Sabrina, Sara Foresti, and Pierangela Samarati. "Data Protection in Cloud Scenarios." In Lecture Notes in Computer Science, 3–10. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29883-2_1.
Повний текст джерелаWei, Yu, and Yongsheng Zhang. "Cloud Computing Data Security Protection Strategy." In Cloud Computing and Security, 376–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00006-6_34.
Повний текст джерелаGarg, Disha. "Privacy in Cloud Healthcare Data." In Data Protection and Privacy in Healthcare, 21–36. Boca Raton, FL : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003048848-2.
Повний текст джерелаLehner, Jonas, Andreas Oberweis, and Gunther Schiefer. "Data Protection in the Cloud - The MimoSecco Approach." In Trusted Cloud Computing, 177–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12718-7_11.
Повний текст джерелаBatman, Ayşe Necibe. "European Cloud Service Data Protection Certification." In Regulating New Technologies in Uncertain Times, 261–80. The Hague: T.M.C. Asser Press, 2019. http://dx.doi.org/10.1007/978-94-6265-279-8_14.
Повний текст джерелаPraveena, D., S. Thanga Ramya, V. P. Gladis Pushparathi, Pratap Bethi, and S. Poopandian. "Hybrid Cloud Data Protection Using Machine Learning Approach." In Studies in Big Data, 151–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75657-4_7.
Повний текст джерелаKunz, Thomas, Annika Selzer, and Ulrich Waldmann. "Automatic Data Protection Certificates for Cloud-Services based on Secure Logging." In Trusted Cloud Computing, 59–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12718-7_5.
Повний текст джерелаCreese, Sadie, Paul Hopkins, Siani Pearson, and Yun Shen. "Data Protection-Aware Design for Cloud Services." In Lecture Notes in Computer Science, 119–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10665-1_11.
Повний текст джерелаLu, Xin, Lifeng Cao, Xuehui Du, and Zhiyan Hu. "A Tag-Based Protection Method for Multi-tenant Data Security." In Cloud Computing and Security, 553–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00009-7_50.
Повний текст джерелаMei, Rui, Han-Bing Yan, Yongqiang He, Qinqin Wang, Shengqiang Zhu, and Weiping Wen. "Considerations on Evaluation of Practical Cloud Data Protection." In Communications in Computer and Information Science, 51–69. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_4.
Повний текст джерелаТези доповідей конференцій з теми "Cloud data protection"
Benjamin, Bruce, Joel Coffman, Hadi Esiely-Barrera, Kaitlin Farr, Dane Fichter, Daniel Genin, Laura Glendenning, et al. "Data Protection in OpenStack." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.77.
Повний текст джерелаMatveev, Artem. "Cost-Efficient Data Privacy Protection in Multi Cloud Storage." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120706.
Повний текст джерелаShi, 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.
Повний текст джерелаAparajit, Srushti, Rashi Shah, Riddhi Chopdekar, and Rupali Patil. "Data Protection: The Cloud Security Perspective." In 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9825151.
Повний текст джерелаHolle, Ronald L., Nicholas W. S. Demetriades, and Amitabh Nag. "Lightning warnings with NLDN cloud and cloud-to-ground lightning data." In 2014 International Conference on Lightning Protection (ICLP). IEEE, 2014. http://dx.doi.org/10.1109/iclp.2014.6973143.
Повний текст джерелаColombo, Maurizio, Rasool Asal, Quang Hieu Hieu, Fadi Ali El-Moussa, Ali Sajjad, and Theo Dimitrakos. "Data Protection as a Service in the Multi-Cloud Environment." In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00025.
Повний текст джерелаMarikyan, Davit, Savvas Papagiannidis, Rajiv Ranjan, and Omer Rana. "General data protection regulation." In UCC '21: 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3492323.3495620.
Повний текст джерелаSinkar, Yogita Deepak, and C. Rajabhushanam. "Data Protection On Cloud Using GWOA Model." In 2021 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. http://dx.doi.org/10.1109/iccci50826.2021.9402346.
Повний текст джерелаChen, Lingfeng, and Doan B. Hoang. "Novel Data Protection Model in Healthcare Cloud." In Communication (HPCC). IEEE, 2011. http://dx.doi.org/10.1109/hpcc.2011.148.
Повний текст джерелаZhang, Wei, Xinwei Sun, and Tao Xu. "Data privacy protection using multiple cloud storages." In 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC). IEEE, 2013. http://dx.doi.org/10.1109/mec.2013.6885340.
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