English

Private Algorithms Can Always Be Extended

Statistics Theory 2018-11-01 v2 Cryptography and Security Data Structures and Algorithms Statistics Theory

Abstract

We consider the following fundamental question on ϵ\epsilon-differential privacy. Consider an arbitrary ϵ\epsilon-differentially private algorithm defined on a subset of the input space. Is it possible to extend it to an ϵ\epsilon'-differentially private algorithm on the whole input space for some ϵ\epsilon' comparable with ϵ\epsilon? In this note we answer affirmatively this question for ϵ=2ϵ\epsilon'=2\epsilon. Our result applies to every input metric space and space of possible outputs. This result originally appeared in a recent paper by the authors [BCSZ18]. We present a self-contained version in this note, in the hopes that it will be broadly useful.

Cite

@article{arxiv.1810.12518,
  title  = {Private Algorithms Can Always Be Extended},
  author = {Christian Borgs and Jennifer Chayes and Adam Smith and Ilias Zadik},
  journal= {arXiv preprint arXiv:1810.12518},
  year   = {2018}
}
R2 v1 2026-06-23T04:57:05.788Z