Connecting Robust Shuffle Privacy and Pan-Privacy
Victor Balcer Harvard University, Supported by NSF grant CNS-1565387 Albert Cheu Northeastern University, Supported by NSF grants CCF-1718088, CCF- 1750640, and CNS-1816028. Matthew Joseph Google New York, Part of this work done while a graduate student at the University of Pennsylvania Jieming Mao Google New York,
Abstract
In the shuffle model of differential privacy, data-holding users send randomized messages to a secure shuffler, the shuffler permutes the messages, and the resulting collection of messages must be differentially private with regard to user data. In the pan-private model, an algorithm processes a stream of data while maintaining an internal state that is differentially private with regard to the stream data. We give evidence connecting these two apparently different models.
原文 arXiv:2004.09481;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2004.09481v4