论文标题
非建立私人功能评估
Non-Stochastic Private Function Evaluation
论文作者
论文摘要
我们考虑私人功能评估以基于多个不信任实体的私人数据提供查询响应，以使每个人都无法对他人数据进行实质性的新知识。首先，我们在两党的情况下介绍了完美的非策略隐私。完美的隐私等于查询响应的有条件无关，以及以给定实体的不确定变量为条件的其他个体的私人不确定变量。我们表明，对于是通用不确定变量的函数的查询，可以实现完美的隐私，这是通用随机变量的概括。我们计算不采用此形式的查询的最接近近似值。为了在隐私和公用事业之间提供权衡取舍，我们放松了完美的隐私概念。我们定义了几乎完美的隐私，并表明该新定义等于在完美隐私的定义中使用条件分离而不是有条件的无关。然后，我们将定义推广到多方功能评估（超过两个数据实体）。我们证明了查询响应的统一量化，其中量化分辨率是隐私预算和查询敏感性的函数（参见，差异隐私），实现了功能评估隐私。
We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially new about the data of others. First, we introduce perfect non-stochastic privacy in a two-party scenario. Perfect privacy amounts to conditional unrelatedness of the query response and the private uncertain variable of other individuals conditioned on the uncertain variable of a given entity. We show that perfect privacy can be achieved for queries that are functions of the common uncertain variable, a generalization of the common random variable. We compute the closest approximation of the queries that do not take this form. To provide a trade-off between privacy and utility, we relax the notion of perfect privacy. We define almost perfect privacy and show that this new definition equates to using conditional disassociation instead of conditional unrelatedness in the definition of perfect privacy. Then, we generalize the definitions to multi-party function evaluation (more than two data entities). We prove that uniform quantization of query responses, where the quantization resolution is a function of privacy budget and sensitivity of the query (cf., differential privacy), achieves function evaluation privacy.
