ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release

• Type of Material: Article
• Authors: Yang Cao, Emory University; Li Xiong, Emory University; Masatoshi Yoshikawa, Kyota University; Yonghui Xiao, Google Inc.; Si Zhang, University of Calgary
• Language: English
• Date: 2018-08
• Publisher: ACM Press
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 ACM Press
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.14778/3229863.3236267
• Title of Journal or Parent Work: Proceedings of the VLDB Endowment
• Volume: 11
• Issue: 12
• Start Page: 2090
• End Page: 2093
• Grant/Funding Agency: National Science Foundation; JSPS KAKENHI; Advanced Research Network; National Institute of Health; Patient-Centered Outcomes Research Institute
• Grant/Funding Information: This work was supported by JSPS KAKENHI Grant Number 16K12437, 17H06099, 18H04093, JSPS Core-to-Core Program, A. Advanced Research Network, the National Institute of Health (NIH) under award number R01GM114612, the Patient-Centered Outcomes Research Institute (PCORI) under contract ME-1310-07058, and the National Science Foundation under award CNS-1618932.
• Keywords: sensitive streaming data; privacy; Differentially Private Continuous Data Release; Temporal Privacy Leakage
• Subject - Topics: Computer security; Stream processing; Data privacy

Abstract

In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve ϵ-differential privacy for processing sensitive streaming data, whose privacy loss is rigorously controlled within a given parameter ϵ. However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by ϵ due to temporal correlations. We call such unexpected privacy loss Temporal Privacy Leakage (TPL). In this demonstration, we design a system, ConTPL, which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator understand and explore the effect of different parameters on TPL.