Privacy-preserving similarity search plays an essential role in data analytics, especially when very large encrypted datasets are stored in the cloud. Existing mechanisms on privacy-preserving similarity search were not able to support secure updates (addition and deletion) efficiently when frequent updates are needed. In this article, we propose a new mechanism to support parallel privacy-preserving similarity search in a distributed key-value store in the cloud, with a focus on efficient addition and deletion operations, both executed with sublinear time complexity. If search accuracy is the top priority, we further leverage Yao's garbled circuits and the homomorphic property of Hash-ElGamal encryption to build a secure evaluation protocol, which can obtain the top-$R$R most accurate results without extensive client-side post-processing. We have formally analyzed the security strength of our proposed approach, and performed an extensive array of experiments to show its superior performance as compared to existing mechanisms in the literature. In particular, we evaluate the performance of our proposed protocol with respect to the time it takes to build the index and perform similarity queries. Extensive experimental results demonstrated that our protocol can speedup the index building process by up to 800× with 2 threads and the similarity queries by up to $\sim $∼7× with comparable accuracy, as compared to the state-of-the-art in the literature.

中文翻译：

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分布式键值存储中具有高效更新的隐私保护相似性搜索

隐私保护相似性搜索在数据分析中起着至关重要的作用，尤其是当非常大的加密数据集存储在云中时。当需要频繁更新时，现有的隐私保护相似性搜索机制无法有效地支持安全更新（添加和删除）。在本文中，我们提出了一种新机制来支持云中分布式键值存储中的并行隐私保护相似性搜索，重点是高效的添加和删除操作，两者都以次线性时间复杂度执行。如果搜索精度是重中之重，我们进一步利用Yao的乱码电路和Hash-ElGamal加密的同态性来构建一个安全的评估协议，可以获得top-$R$电阻无需大量客户端后处理即可获得最准确的结果。我们已经正式分析了我们提出的方法的安全强度，并进行了大量实验以证明其与文献中现有机制相比的优越性能。特别是，我们根据构建索引和执行相似性查询所需的时间来评估我们提出的协议的性能。大量的实验结果表明，我们的协议可以使用 2 个线程将索引构建过程加快多达 800 倍，并将相似性查询加快多达$\sim $～与文献中的最新技术相比，具有可比精度的 7 倍。