1,627 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
Advances in Information Security and Privacy
With the recent pandemic emergency, many people are spending their days in smart working and have increased their use of digital resources for both work and entertainment. The result is that the amount of digital information handled online is dramatically increased, and we can observe a significant increase in the number of attacks, breaches, and hacks. This Special Issue aims to establish the state of the art in protecting information by mitigating information risks. This objective is reached by presenting both surveys on specific topics and original approaches and solutions to specific problems. In total, 16 papers have been published in this Special Issue
Privacy-preserving queries on encrypted databases
In today's Internet, with the advent of cloud computing, there is a natural desire for enterprises, organizations, and end users to outsource increasingly large amounts of data to a cloud provider. Therefore, ensuring security and privacy is becoming a significant challenge for cloud computing, especially for users with sensitive and valuable data. Recently, many efficient and scalable query processing methods over encrypted data have been proposed. Despite that, numerous challenges remain to be addressed due to the high complexity of many important queries on encrypted large-scale datasets. This thesis studies the problem of privacy-preserving database query processing on structured data (e.g., relational and graph databases). In particular, this thesis proposes several practical and provable secure structured encryption schemes that allow the data owner to encrypt data without losing the ability to query and retrieve it efficiently for authorized clients. This thesis includes two parts. The first part investigates graph encryption schemes. This thesis proposes a graph encryption scheme for approximate shortest distance queries. Such scheme allows the client to query the shortest distance between two nodes in an encrypted graph securely and efficiently. Moreover, this thesis also explores how the techniques can be applied to other graph queries. The second part of this thesis proposes secure top-k query processing schemes on encrypted relational databases. Furthermore, the thesis develops a scheme for the top-k join queries over multiple encrypted relations. Finally, this thesis demonstrates the practicality of the proposed encryption schemes by prototyping the encryption systems to perform queries on real-world encrypted datasets
Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges
In the last decade, Federated Learning (FL) has gained relevance in training
collaborative models without sharing sensitive data. Since its birth,
Centralized FL (CFL) has been the most common approach in the literature, where
a central entity creates a global model. However, a centralized approach leads
to increased latency due to bottlenecks, heightened vulnerability to system
failures, and trustworthiness concerns affecting the entity responsible for the
global model creation. Decentralized Federated Learning (DFL) emerged to
address these concerns by promoting decentralized model aggregation and
minimizing reliance on centralized architectures. However, despite the work
done in DFL, the literature has not (i) studied the main aspects
differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and
evaluate new solutions; and (iii) reviewed application scenarios using DFL.
Thus, this article identifies and analyzes the main fundamentals of DFL in
terms of federation architectures, topologies, communication mechanisms,
security approaches, and key performance indicators. Additionally, the paper at
hand explores existing mechanisms to optimize critical DFL fundamentals. Then,
the most relevant features of the current DFL frameworks are reviewed and
compared. After that, it analyzes the most used DFL application scenarios,
identifying solutions based on the fundamentals and frameworks previously
defined. Finally, the evolution of existing DFL solutions is studied to provide
a list of trends, lessons learned, and open challenges
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