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Pure Differentially Private Algorithms for Summation in the Shuffled Model

机译:纯差别私有算法,用于播放模型中的求和

摘要

An encoding method for enabling privacy-preserving aggregation of private data can include obtaining private data including a private value, determining a probabilistic status defining one of a first condition and a second condition, producing a multiset including a plurality of multiset values, and providing the multiset for aggregation with a plurality of additional multisets respectively generated for a plurality of additional private values. In response to the probabilistic status having the first condition, the plurality of multiset values is based at least in part on the private value, and in response to the probabilistic status having the second condition, the plurality of multiset values is a noise message. The noise message is produced based at least in part on a noise distribution that comprises a discretization of a continuous unimodal distribution supported on a range from zero to a number of multiset values included in the plurality of multiset values.
机译:用于启用私有数据的隐私聚合的编码方法可以包括获取包括私有值的私有数据,确定定义第一条件和第二条件中的一个的概率状态,产生包括多个多重值的多项数据,并提供用于聚合的聚合,用于分别为多个附加私有值生成多个附加的多重。响应于具有第一条件的概率状态,多个多网值至少部分地基于私有值,并且响应于具有第二条件的概率状态,多个多网是噪声消息。至少部分地基于噪声分布来生产噪声消息,该噪声分布包括在从零到多个多网值中包括的范围内支持的连续单向分布的离散化的离散化。

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