Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation

Bo Jiang, Ming Li, Ravi Tandon

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The concept of context-awareness is incorporated in LIP, which can be viewed as exploiting of data prior (both in privatizing and post-processing) to enhance data utility. We present an optimization framework to minimize the mean square error of data aggregation while protecting the privacy of each user's input data or a correlated latent variable by satisfying LIP constraints. Then, we study optimal mechanisms under different scenarios considering the prior uncertainty and correlation with a latent variable. Three types of mechanisms are studied in this paper, including randomized response (RR), unary encoding (UE), and local hashing (LH), and we derive closed-form solutions for the optimal perturbation parameters that are prior-dependent. We compare LIP based mechanisms with those based on LDP, and theoretically show that the former achieves enhanced utility. We then study two applications: (weighted) summation and histogram estimation, and show how proposed mechanisms can be applied to each application. Finally, we validate our analysis by simulations using both synthetic and real-world data.

Original languageEnglish (US)
JournalIEEE Transactions on Dependable and Secure Computing
DOIs
StateAccepted/In press - 2020

Keywords

  • information-theoretic privacy
  • local information privacy
  • privacy-preserving data aggregation

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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