使用完全同态非确定性加密的大数据隐私

Tejas Patil, G. Patnaik, A. T. Bhole
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引用次数: 7

摘要

大数据是指大量的数字信息。如今,数据安全是一个具有挑战性的问题,涉及到计算机和通信的几个领域。存储在网上的数据的安全性已成为一个主要问题。一些攻击者利用用户的机密性。密码学是一种为用户提供数据安全性的方法。尽管在保护敏感数据方面付出了巨大的努力,但黑客通常还是能窃取到这些数据。使用加密数据进行计算是保护机密数据的策略。部分同态加密专门用于对加密数据的一次操作。例如,Pailliers加密方案仅对加密的数字数据执行一次数学运算,并成功地计算出加密值的总和。paillier加密方案不能对加密的数值数据进行多次数学运算。所提出的加密算法对加密的数字数据计算多个数学运算,从而进一步保护加密的敏感信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data Privacy Using Fully Homomorphic Non-Deterministic Encryption
Big data is a large amount of digital information. Now days, data security is a challenging issue that touches several areas along with computers and communication. The security of data which stored online has become a main concern. Several attackers play with confidentiality of the user. Cryptography is a approach that provide data security to the user. Despite of huge efforts to protect sensitive data, hackers typically manage to steal it. Computing with encrypted data is strategies for safeguarding confidential data. The partial homomorphic encryption is specialized for only one operation on the encrypted data. For example the Pailliers encryption scheme performs only one mathematical operation on encrypted numerical data and is successful to compute the sum of encrypted values. The Pailliers encryption scheme is unable to do multiple mathematical operations on encrypted numerical data. The proposed encryption algorithm computes more than one mathematical operation on encrypted numerical data thereby further protecting the encrypted sensitive information.
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