Inherit Differential Privacy in Distributed Setting: Multiparty Randomized Function Computation
Inherit Differential Privacy in Distributed Setting: Multiparty Randomized Function Computation
复制标题
分布式环境中继承差分隐私：多方随机函数计算
DOI：
10.1109/trustcom.2016.0157
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Xianyao Xia
中科院分区：
文献类型：
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作者：
Genqiang Wu;Yeping He;Jingzheng Wu;Xianyao Xia
如何在分布式环境中实现差异隐私，其中数据集分布在不信任的各方之间，是一个重要的问题。我们考虑在什么条件下协议可以继承它计算的函数的差分隐私属性。问题的核心是随机函数的安全多方计算。一个概念遗忘的介绍，它抓住了关键的安全问题时，从一个确定性的分布设置计算一个随机化的功能。通过这一观察，给出了从确定性函数安全地计算随机化函数的一个充要条件。上述结果不仅可以用来判断一个计算差分私有函数的协议是否安全，而且可以用来构造一个安全的差分私有函数协议。然后证明了如果协议安全地计算了一个函数，则计算该函数的协议可以继承该函数的差分隐私性质，并给出了差分隐私协议的合成定理。最后，我们构造了高斯机制和拉普拉斯机制的协议，它们继承了差分隐私特性。
How to achieve differential privacy in the distributed setting, where the dataset is distributed among the istrustful parties, is an important problem. We consider in what condition can a protocol inherit the differential privacy property of a function it computes. The heart of the problem is the secure multiparty computation of randomized function. A notion obliviousness is introduced, which captures the key security problems when computing a randomized function from a deterministic one in the distributed setting. By this observation, a sufficient and necessary condition about securely computing a randomized function from a deterministic one is given. The above result can not only be used to determine whether a protocol computing differentially private function is secure, but also be used to construct a secure one. Then we prove that the differential privacy property of a function can be inherited by the protocol computing it if the protocol securely computes it. A composition theorem of differentially private protocols is also presented. Finally, we construct protocols of Gaussian mechanism and Laplace mechanism, which inherit the differential privacy property.