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Implementation, Optimization and Performance Tests of Privacy Preserving Mechanisms in Homogeneous Collaborative Association Rules Mining

机译:同类协同关联规则挖掘中隐私保护机制的实现,优化和性能测试

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摘要

This article focuses on optimization and performance tests of association rules multiparty mining algorithms. We present how to improve Secure Set Union performance by using a Common Decrypting Key for a commutative encryption. We have also improved Secure Set Union making it fully secure in a semi-honest model. As an example of the above mentioned mechanisms application, the article presents new algorithms of mining association rules on horizontally partitioned data with preserving data privacy - CDKSU (Secure Union with Common Decrypting Key) and its fully secure version - CDKRSU (Secure Union with Common Decrypting Key and secure duplicate Removing). Those algorithms are compared with KCS scheme since they are all based on FDM. As far as the performance optimization is concerned, the application of Elliptic Curve Cryptography vs Exponential Cryptography is presented as well. The real, working system implementing given algorithms is subjected to performance tests which results are presented and analyzed.
机译:本文重点介绍关联规则多方挖掘算法的优化和性能测试。我们介绍了如何通过使用通用解密密钥进行可交换加密来提高安全集联合性能。我们还改进了Secure Set Union,使其在半诚实的模型中完全安全。作为上述机制应用程序的示例,本文介绍了在保留数据隐私的情况下对水平分区数据挖掘关联规则的新算法-CDKSU(具有公共解密密钥的安全联盟)及其完全安全的版本-CDKRSU(具有公共解密密钥的安全联盟)密钥和安全副本(删除)。这些算法都基于FDM,因此可以与KCS方案进行比较。就性能优化而言,还介绍了椭圆曲线密码术与指数密码术的应用。实施给定算法的实际工作系统要进行性能测试,并给出结果并进行分析。

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