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Privacy-Preserving Cooperative Statistical Analysis

机译:保留隐私合作统计分析

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The growth of the Internet opens up tremendous opportunities for cooperative computation, where the answer depends on the private inputs of separate entities. Sometimes these computations may occur between mutually untrusting entities. The problem is trivial if the context allows the conduct of these computations by a trusted entity that would know the inputs from all the participants; however if the context disallows this then the techniques of secure multi-party computation become very relevant and can provide useful solutions. Statistic analysis is a widely used computation in real life, but the known methods usually require one to know the whole data set; little work has been conducted to investigate how statistical analysis could be performed in a cooperative environment, where the participants want to conduct statistical analysis on the joint data set, but each participant is concerned about the confidentiality of its own data. In this paper we have developed protocols for conducting the statistic analysis in such kind of cooperative environment based on a data perturbation technique and cryptography primitives.
机译:互联网的增长为合作计算开辟了巨大的机会,其中答案取决于单独实体的私人输入。有时,可以在相互不可信的实体之间发生这些计算。如果上下文允许通过可信实体进行这些计算,则该问题是微不足道的,这将是从所有参与者知道输入的值;但是,如果上下文不允许这一点,则安全多方计算的技术变得非常相关,并且可以提供有用的解决方案。统计分析是在现实生活中广泛使用的计算,但已知的方法通常需要一个了解整个数据集;已经进行了很少的工作来调查如何在合作环境中进行统计分析,其中参与者希望对联合数据集进行统计分析,但每个参与者都关注其自身数据的机密性。在本文中,我们已经开发了基于数据扰动技术和密码基因的这种合作环境中的统计分析的协议。

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