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Reducing communication cost of encrypted data search with compressed bloom filters

机译:使用压缩的布隆过滤器降低加密数据搜索的通信成本

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Consumer data, such as documents and photos, are increasingly being stored in the cloud. To ensure confidentiality, the data is encrypted before being outsourced. However, this makes it difficult to search the encrypted data directly. Recent approaches to enable searching of this encrypted data have relied on trapdoors, locally stored indexes, and homomorphic encryption. However, data communication cost for these techniques is very high and they limit search capabilities either to a small set of pre-defined trapdoors or only allow exact keyword matching. This paper addresses the problem of high communication cost of bloom-filter based privacy-preserving search for measuring similarity between the search query and the encrypted data. We propose a novel compression algorithm which avoids the need to send the entire encrypted bloom filter index back to the client. This reduces the cost of communicating results to the client by over 95%. Using sliding window bloom filters and homomorphic encryption also enables our system to search over encrypted data using keywords that only partially match the originally stored words. We demonstrate the viability of our system by implementing it on Google Cloud, and our results show that the cost of partially matching search queries on encrypted data scales linearly with the total number of keywords stored on the server.
机译:消费者数据(例如文档和照片)越来越多地存储在云中。为确保机密性,数据在外包之前已加密。但是,这使得直接搜索加密数据变得困难。用于搜索此加密数据的最新方法依赖于活板门,本地存储的索引和同态加密。但是,这些技术的数据通信成本非常高,并且它们将搜索功能限制为一小组预定义的陷门或仅允许精确的关键字匹配。本文针对用于测量搜索查询与加密数据之间的相似度的基于布鲁姆过滤器的隐私保护搜索,解决了通信成本高的问题。我们提出了一种新颖的压缩算法,该算法无需将整个加密的布隆过滤器索引发送回客户端。这将与客户沟通结果的成本降低了95%以上。通过使用滑动窗口绽放过滤器和同态加密,我们的系统还可以使用仅部分匹配原始存储单词的关键字来搜索加密数据。我们通过在Google Cloud上实施该系统来证明我们系统的可行性,我们的结果表明,部分匹配加密数据上的搜索查询的成本与服务器上存储的关键字总数呈线性比例关系。

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