Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances

Author:

Savitha N.1,Sai Kiran E.1

Affiliation:

1. Department of Computer Science and Engineering, Chaintanya Deemed to be University, Warangal, Telangana, India

Abstract

Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confidentiality and integrity of data in the cloud, as the data is stored on third-party servers. To address these concerns, various privacy-preserving techniques have been proposed, which allow users to store and process their data in the cloud without compromising privacy and security. We provide a thorough overview of current developments in privacy-preserving methods for safe cloud computing in this study. We start by giving a general review of cloud computing and the security issues it presents. Then, we go over a variety of privacy-preserving methods, such as differential privacy, homomorphic encryption, secure outsourcing, and secure multi-party computation. We also highlight their advantages and limitations. Finally, we conclude with some future research directions in privacy-preserving cloud computing.

Publisher

Technoscience Academy

Subject

General Medicine

Reference38 articles.

1. A. Juels and R. L. Rivest, “Honey encryption: security beyond the brute-force bound,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques, Springer, pp. 59–79, 2014.

2. A. Kamara and A. Lauter, "Cryptographic Cloud Storage," in Financial Cryptography and Data Security, 2010, pp. 136-149.

3. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics, pp. 1273–1282, 2017.

4. C. C. Aggarwal, “Data privacy in the information age,” Springer, 2015.

5. C. Dwork, “Differential privacy,” in Automata, Languages and Programming, Springer, pp. 1–12, 2006.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3