Limiting Privacy Breaches in Differential Privacy

DOI

10.2991/csss-14.2014.153How to use a DOI?

Keywords

component; differential privacy;privacy breaches; privacy-preserving data mining

Abstract

In recently years, privacy-preserving data mining has become more import and attractedmore attention from data mining community. Among the existing privacy preserving models, -differential privacy provides the strongest privacy guarantees and has no assumption about the adversary’s background information and compute ability. However, howto set to satisfy privacy is still an open problem. In this paper, we propose a tactic, named LPB (Limiting Privacy Breaches), to set the privacy parameter intuitively. LPB ensures that, if the prior belief about individual is bounded by some threshold, the posterior belief, after given the published randomized result, is no more than another threshold.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ouyang Jia
AU  - Yin Jian
AU  - Liu Shaopeng
PY  - 2014/06
DA  - 2014/06
TI  - Limiting Privacy Breaches in Differential Privacy
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 657
EP  - 664
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.153
DO  - 10.2991/csss-14.2014.153
ID  - Jia2014/06
ER  -