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Secure Optimal k-NN on Encrypted Cloud Data using Homomorphic Encryption with Query Users

机译:使用具有查询用户的同态加密,在加密的云数据上确保最佳的k-NN安全

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In cloud computing, research on security issues among outsourced encrypted data is trending topic. It has broad applications in area-based management, classification, and clustering. As any other normal utilized query for online applications, secure k-Nearest Neighbors (k-NN) calculation on encrypted cloud data is highly being considered now a days, and a few advanced answers have been produced. This paper proposed an innovative plan for encrypting the outsourced database and query points. The new plan can adequately support k-Nearest Neighbor (KNN) computation while preserving data privacy and query privacy. To improve the performance of the system, the Opposition-based Particle Swarm Optimization (OPSO) optimization algorithm is utilized to secure the data by Homomorphic Encryption (HE) method. The broad hypothetical and test assessments exhibit the adequacy of our plan with regards to security and performance.
机译:在云计算中,对外包加密数据中的安全性问题的研究已成为热门话题。它在基于区域的管理,分类和群集中具有广泛的应用。与其他任何在线应用程序正常使用的查询一样,如今已经高度重视对加密的云数据进行安全的k最近邻(k-NN)计算,并且已经提出了一些高级答案。本文提出了一种对外包数据库和查询点进行加密的创新计划。新计划可以充分支持k最近邻(KNN)计算,同时保留数据隐私和查询隐私。为了提高系统的性能,利用基于对立的粒子群优化(OPSO)优化算法通过同态加密(HE)方法来保护数据。广泛的假设和测试评估显示了我们计划在安全性和性能方面的适当性。

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