@article{Rassouli:2019,
author = {Rassouli, B and Rosas, FE and Gunduz, D},
title = {Data disclosure under perfect sample privacy},
url = {http://arxiv.org/abs/1904.01711v1},
year = {2019}
}

Download

TY  - JOUR
AB  - Perfect data privacy seems to be in fundamental opposition to the economicaland scientific opportunities associated with extensive data exchange. Defyingthis intuition, this paper develops a framework that allows the disclosure ofcollective properties of datasets without compromising the privacy ofindividual data samples. We present an algorithm to build an optimal disclosurestrategy/mapping, and discuss it fundamental limits on finite andasymptotically large datasets. Furthermore, we present explicit expressions tothe asymptotic performance of this scheme in some scenarios, and study caseswhere our approach attains maximal efficiency. We finally discuss suboptimalschemes to provide sample privacy guarantees to large datasets with a reducedcomputational cost.
AU  - Rassouli,B
AU  - Rosas,FE
AU  - Gunduz,D
PY  - 2019///
TI  - Data disclosure under perfect sample privacy
UR  - http://arxiv.org/abs/1904.01711v1
UR  - http://hdl.handle.net/10044/1/73323
ER  - 

Download