基于增广同态再加密解密(AHRED)算法的大数据分析数据安全和隐私保护

V. Shoba, R. Parameswari
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引用次数: 1

摘要

由于大量数据的扩展,大数据的存储过程变得具有挑战性;数据提供商将提供加密数据并上传到大数据。但是,数据交换机制无法容纳加密的数据。特别是当大量用户共享可扩展数据时,可伸缩性变得非常有限。采用现代的隐私保护系统解决了这一问题,保证了加密数据的安全性,以及部分同态的再加解密(PHRED)。该方案通过部分可信大数据保障用户隐私,实现数据共享的灵活性。它可以获得强不可伪造方案,使转换后的密文具有公钥和私钥验证,结合基于身份的增强同态再加密解密(AHRED),在具有拉普拉斯噪声滤波的paillier密码系统上实现数据提供者的性能,实现了大数据的隐私保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics
The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data.
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