文章基本信息

标题：Optimized Homomorphic Scheme on Map Reduce for Data Privacy Preserving
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作者：Konan Martin ; Wenyong Wang ; Brighter Agyemang 等
期刊名称：Journal of Information Security
印刷版ISSN：2153-1234
电子版ISSN：2153-1242
出版年度：2017
卷号：08
期号：03
页码：257-273
DOI：10.4236/jis.2017.83017
语种：English
出版社：Scientific Research Publishing
摘要：Security insurance is a paramount cloud services issue in the most recent decade. Therefore, Mapreduce which is a programming framework for preparing and creating huge data collections should be optimized and securely implemented. But, conventional operations on ciphertexts were not relevant. So there is a foremost need to enable particular sorts of calculations to be done on encrypted data and additionally optimize data processing at the Map stage. Thereby schemes like (DGHV) and (Gen 10) are presented to address data privacy issue. However private encryption key (DGHV) or key’s parameters (Gen 10) are sent to untrusted cloud server which compromise the information security insurance. Therefore, in this paper we propose an optimized homomorphic scheme (Op_FHE_SHCR) which speed up ciphertext (R_c) retrieval and addresses metadata dynamics and authentication through our secure Anonymiser agent. Additionally for the efficiency of our proposed scheme regarding computation cost and security investigation, we utilize a scalar homomorphic approach instead of applying a blinding probabilistic and polynomial-time calculation which is computationally expensive. Doing as such, we apply an optimized ternary search tries (TST) algorithm in our metadata repository which utilizes Merkle hash tree structure to manage metadata authentication and dynamics.
关键词：Privacy;Mapreduce;Homomorphic Encryption;Ciphertexts Retrieval;Optimization;Authentication