Optimal noise adding mechanisms for approximate differential privacy

Quan Geng, Pramod Viswanath

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

We study the (nearly) optimal mechanisms in (∈, δ)-differential privacy for integer-valued query functions and vector-valued (histogram-like) query functions under a utilitymaximization/ cost-minimization framework. Within the classes of mechanisms oblivious of the database and the queries beyond the global sensitivity, we characterize the tradeoff between ∈ and δ in utility and privacy analysis for histogram-like query functions, and show that the (∈, δ)-differential privacy is a framework not much more general than the (∈, 0)-differential privacy and (0, δ)-differential privacy in the context of ℓ1 and ℓ2 cost functions, i.e., minimum expected noise magnitude and noise power. In the same context of ℓ1 and ℓ2 cost functions, we show the near-optimality of uniform noise mechanism and discrete Laplacian mechanism in the high privacy regime (as (∈, δ) → (0, 0)). We conclude that in (∈, δ)-differential privacy, the optimal noise magnitude and the noise power are Θ(min((1/∈), (1/δ))) and Θ(min((1/∈2), (1/δ2))), respectively, in the high privacy regime.

Original languageEnglish (US)
Article number2504972
Pages (from-to)952-969
Number of pages18
JournalIEEE Transactions on Information Theory
Volume62
Issue number2
DOIs
StatePublished - Feb 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

Keywords

  • Data privacy
  • Randomized algorithm

Fingerprint

Dive into the research topics of 'Optimal noise adding mechanisms for approximate differential privacy'. Together they form a unique fingerprint.

Cite this