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 -differential privacy. Consider an arbitrary -differentially private algorithm defined on a subset of the input space. Is it possible to extend it to an -differentially private algorithm on the whole input space for some comparable with ? In this note we answer affirmatively this question for . 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}
}