Title :
Geometry of privacy and utility
Author :
Bing-Rong Lin ; Kifer, D.
Author_Institution :
Penn State Univ., University Park, PA, USA
Abstract :
One of the important challenges in statistical privacy is the design of algorithms that maximize a utility measure subject to restrictions imposed by privacy considerations. In this paper we examine large classes of privacy definitions and utility measures. We identify their geometric characteristics and some common properties of optimal privacy-preserving algorithms.
Keywords :
data privacy; geometry; algorithm design; geometric characteristics; privacy considerations; privacy definitions; privacy-preserving algorithms; statistical privacy; utility measure; Algorithm design and analysis; Atmospheric measurements; Data privacy; Equations; Geometry; Privacy; Vectors;
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
DOI :
10.1109/GlobalSIP.2013.6736870
