Limiting Privacy Breaches in Differential Privacy
Ouyang Jia, Yin Jian, Liu Shaopeng
Available Online June 2014.
- 10.2991/csss-14.2014.153How to use a DOI?
- component; differential privacy;privacy breaches; privacy-preserving data mining
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.
- © 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/).
Cite this article
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 -