445 research outputs found
Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions
Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discussed
Data auditing and security in cloud computing: issues, challenges and future directions
Cloud computing is one of the significant development that utilizes progressive computational power and
upgrades data distribution and data storing facilities. With cloud information services, it is essential for
information to be saved in the cloud and also distributed across numerous customers. Cloud information
repository is involved with issues of information integrity, data security and information access by unapproved
users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is
effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art
techniques in data auditing and security are discussed. Challenging problems in information repository auditing
and security are presented. Finally, directions for future research in data auditing and security have been
discusse
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
IoT Expunge: Implementing Verifiable Retention of IoT Data
The growing deployment of Internet of Things (IoT) systems aims to ease the
daily life of end-users by providing several value-added services. However, IoT
systems may capture and store sensitive, personal data about individuals in the
cloud, thereby jeopardizing user-privacy. Emerging legislation, such as
California's CalOPPA and GDPR in Europe, support strong privacy laws to protect
an individual's data in the cloud. One such law relates to strict enforcement
of data retention policies. This paper proposes a framework, entitled IoT
Expunge that allows sensor data providers to store the data in cloud platforms
that will ensure enforcement of retention policies. Additionally, the cloud
provider produces verifiable proofs of its adherence to the retention policies.
Experimental results on a real-world smart building testbed show that IoT
Expunge imposes minimal overheads to the user to verify the data against data
retention policies.Comment: This paper has been accepted in 10th ACM Conference on Data and
Application Security and Privacy (CODASPY), 202
Privacy-Preserving Secret Shared Computations using MapReduce
Data outsourcing allows data owners to keep their data at \emph{untrusted}
clouds that do not ensure the privacy of data and/or computations. One useful
framework for fault-tolerant data processing in a distributed fashion is
MapReduce, which was developed for \emph{trusted} private clouds. This paper
presents algorithms for data outsourcing based on Shamir's secret-sharing
scheme and for executing privacy-preserving SQL queries such as count,
selection including range selection, projection, and join while using MapReduce
as an underlying programming model. Our proposed algorithms prevent an
adversary from knowing the database or the query while also preventing
output-size and access-pattern attacks. Interestingly, our algorithms do not
involve the database owner, which only creates and distributes secret-shares
once, in answering any query, and hence, the database owner also cannot learn
the query. Logically and experimentally, we evaluate the efficiency of the
algorithms on the following parameters: (\textit{i}) the number of
communication rounds (between a user and a server), (\textit{ii}) the total
amount of bit flow (between a user and a server), and (\textit{iii}) the
computational load at the user and the server.\BComment: IEEE Transactions on Dependable and Secure Computing, Accepted 01
Aug. 201
- …