A novel privacy-preserving scheme for collaborative frequent itemset mining across vertically partitioned data
A novel privacy-preserving scheme for collaborative frequent itemset mining across vertically partitioned data
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DOI：
10.1002/sec.1377
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发表时间：
2015-12-01
影响因子：
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通讯作者：
Jinwala, Devesh C.
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文献类型：
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作者：
Nanavati, Nirali R.;Jinwala, Devesh C.
协同数据挖掘中的隐私保护是一个重要的研究问题。垂直分区数据模型是一种重要的数据分区模型，具有多种应用。垂直分割的数据模型需要一种非合谋的方案和一种有效的方案来解决保护隐私的分布式频繁项集挖掘问题。目前文献中有基于安全和、集合交集基数和安全二进制点积（SBDP）的PPDFIM方案。[m，m] Shamir的加性秘密共享已被提出作为PPDFIM在垂直分区设置中使用安全求和子协议的非共谋方案。然而，这种方案在分布式频繁项集挖掘场景中会导致信息泄露，无法达到保护隐私的目的。对垂直分割模型中频繁项集挖掘中使用的基于非合谋秘密共享的隐私保护方法进行了批评。在此基础上，提出了PPDFIM中两个向量的高效乘法协议。我们还提出了非合谋Du-Atallah的SBDP协议的扩展，用于垂直分区设置，以挖掘多方多向量场景的频繁项集。我们展示了这种抗合谋方案如何不会导致隐私损失，并给出了理论和实证分析。此外，我们表明，就多方场景的执行成本而言，我们提出的方案比Vaidya等人提出的基于公钥的开创性方案更有效。版权所有：John Wiley & Sons， Ltd
Privacy preservation while undertaking collaborative data mining is a significant research problem. The vertically partitioned data model is an important data partition model and has varied applications. The vertically partitioned data model necessitates a non-collusive scheme and an efficient scheme for the problem of privacy-preserving distributed frequent itemset mining (PPDFIM). The current literature has schemes based on secure sum, set intersection cardinality and secure binary dot product (SBDP) for PPDFIM across vertically partitioned data. [m,m] Shamir's additive secret sharing has been proposed as a non-collusive scheme for PPDFIM in a vertically partitioned setup that uses the secure sum sub-protocol. However, such a scheme leads to information leakage in the distributed frequent itemset mining scenario and defeats the purpose of privacy preservation. We give a critique on the non-collusive secret sharing-based approaches when used for privacy preservation in frequent itemset mining in a vertically partitioned model. Further, we propose Du-Atallah's efficient multiplication protocol for SBDP of two vectors for PPDFIM. We also propose an extension of the non-collusive Du-Atallah's SBDP protocol for a vertically partitioned setup to mine frequent itemsets for a multi-party multi-vector scenario. We show how such a collusion-resistant scheme does not lead to loss of privacy and give the theoretical and empirical analysis therein. Further, we show that our proposed scheme is more efficient than the seminal public key-based scheme proposed by Vaidya et al. in terms of the execution cost for a multi-party scenario. Copyright (C) 2015 John Wiley & Sons, Ltd.