Advances in communications, storage and computational technology allow significant quantities of data to be collected and processed by distributed devices. Combining the information from these endpoints can realize significant societal benefit but presents challenges in protecting the privacy of individuals, especially important in an increasingly regulated world. Differential privacy (DP) is a technique that provides a rigorous and provable privacy guarantee for aggregation and release. The Shuffle Model for DP has been introduced to overcome challenges regarding the accuracy of local-DP algorithms and the privacy risks of central-DP. In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model. The aim of this paper is twofold; first, we provide a single message protocol for the summation of real vectors in the Shuffle Model, using advanced composition results. Secondly, we provide an improvement on the bound on the error achieved through using this protocol through the implementation of a Discrete Fourier Transform, thereby minimizing the initial error at the expense of the loss in accuracy through the transformation itself. This work will further the exploration of more sophisticated structures such as matrices and higher-dimensional tensors in this context, both of which are reliant on the functionality of the vector case.
翻译:通信、存储和计算技术的进步使得大量数据能够通过分布式设备收集和处理。将来自这些端点的信息合并起来,可以实现巨大的社会效益,但在保护个人隐私方面提出了挑战,在日益受监管的世界中尤其重要。差异隐私(DP)是一种技术,为聚合和释放提供了严格和可验证的隐私保障。引入了“DP打字模型”,以克服与当地-DP算法的准确性和中央-DP的隐私风险有关的挑战。在这项工作中,我们引入了一个新的矢量集成协议。本文件的目的是双重的;首先,我们提供了一个单一的信息协议,用于在舒夫勒模型中对真实矢量进行汇总,使用先进的构成结果。第二,我们改进了通过实施“分立的四面形变换”程序而通过使用这一协议所实现的错误的界限,从而将最初的错误降到最低程度,从而降低在变换过程中损失的准确性。这项工作将进一步探索更复杂的结构,如矩阵和更高维度的抗压器,两者都依靠病媒的功能。