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Secure similarity coefficients computation for binary data and its extensions

机译:二进制数据及其扩展的安全相似系数计算

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Similarity measures play an important role in classification problems, cluster analysis, and identificationrnissues. This paper studies the secure similarity coefficients computation in the two-party setting. Recently,rna privacy-preserving similarity coefficients protocol for binary data was proposed by Wong and Kim (Computersrnand Mathematics with Application 2012). We point out that their protocol is not secure, even inrnthe semi-honest model. In their protocol, the client can retrieve the inputs of the server without deviatingrnfrom the protocol. Next, we propose a secure similarity coefficients computation protocol in the presencernof malicious adversaries, which solves the same similarity coefficients functionality as that proposed byrnWong and Kim. Meanwhile, we prove the protocol secure against the malicious adversaries by using thernstandard simulation-based security definitions for secure two-party computation. Also several extensions ofrnour protocol for settling other specific problems are discussed. At last, we present a protocol computingrnthe similarity coefficients with better privacy by using the secure integer division on ciphertexts.
机译:相似性度量在分类问题,聚类分析和识别问题中起着重要作用。本文研究了两方环境下的安全相似系数计算。最近,Wong和Kim(Computersrnand Mathematics with Application 2012)提出了一种用于二进制数据的rna隐私保护相似系数协议。我们指出,即使在半诚实的模型中,它们的协议也不安全。在他们的协议中,客户端可以检索服务器的输入而不会偏离协议。接下来,我们提出了一种在场恶意对手的安全相似系数计算协议,该协议解决了与rnWong和Kim提出的相似系数功能相同的问题。同时,我们通过使用基于标准模拟的安全定义来进行安全的两方计算,证明该协议可抵御恶意对手。还讨论了用于解决其他特定问题的nourour协议的几个扩展。最后,我们提出了一种在密文上使用安全整数除法来计算具有更好保密性的相似系数的协议。

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