A Comparative Study of Privacy-Preserving Homomorphic Encryption Techniques in Cloud Computing
Author:
Affiliation:
1. Swami Rama Himalayan University, India
2. Jaypee Institute of Information Technology, India
3. Insights2Techinfo, India & Lebanese American University, Beirut, Lebanon
4. Karnavati University, India
5. University of Zagreb, Croatia
Abstract
In cloud computing, a third party hosts a client's data, which raises privacy and security concerns. To maintain privacy, data should be encrypted by cryptographic techniques. However, encrypting the data makes it unsuitable for indexing and fast processing, as data needs to be decrypted to plain text before it can be further processed. Homomorphic encryption helps to overcome this shortcoming by allowing users to perform operations on encrypted data without decryption. Many academics have attempted to address the issue of data security, but none have addressed the issue of data privacy in cloud computing as thoroughly as this study has. This paper discusses the challenges involved in maintaining the privacy of cloud-based data and the techniques used to address these challenges. It was identified that homomorphic encryption is the best solution of all. This work also identified and compared the various homomorphic encryption schemes which are capable of ensuring the privacy of data in cloud storage and ways to implement them through libraries.
Publisher
IGI Global
Subject
Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction
Reference36 articles.
1. Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing
2. AWS Cryptographic Computing. (2020, May 15). Amazon Web Services, Inc. Retrieved July 20, 2022, from https://aws.amazon.com/security/cryptographic-computing/
3. Evaluating 2-DNF Formulas on Ciphertexts
4. Brakerski, Z. (2011). Fully Homomorphic Encryption without Bootstrapping. https://eprint.iacr.org/2011/277
5. Burt, J. (2021, June 24). Homomorphic Encryption Makes Real-World Gains, Pushed by Google, IBM, Microsoft. eSecurityPlanet. https://www.esecurityplanet.com/compliance/homomorphic-encryption-makes-real-world-gains/
Cited by 23 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A certificateless and KGA-secure searchable encryption scheme with constant trapdoors in smart city;Digital Communications and Networks;2024-08
2. Enhancing Data Security and Privacy through Classification-Based Adaptive Encryption;2024 IEEE World AI IoT Congress (AIIoT);2024-05-29
3. A Novel Deep Learning Based Gender Classification, Expression Recognition and Emoticon Generation;2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT);2024-02-09
4. An image encryption algorithm for visually meaningful ciphertext based on adaptive compressed, 2D-IICM hyperchaos and histogram cyclic shift;Multimedia Tools and Applications;2024-01-13
5. Simplified Image Encryption Algorithm (SIEA) to enhance image security in cloud storage;Multimedia Tools and Applications;2024-01-06
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