Privacy Notions for Data Publishing and Analysis

Feb 18, 2015

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Abstract

Data collected by organizations and agencies are a key resource in
today's information age. The use of sophisticated data mining techniques
makes it possible to extract relevant knowledge that can then be used for a
variety of purposes, such as research, developing innovative technologies
and services, intelligence and counterterrorism operations, and providing
inputs to public policy making. However the disclosure of those data poses
serious threats to individual privacy.

       In this talk, we will present the evolvement of privacy notions for
data publishing and analysis, leading to our proposed membership privacy
framework, which formalizes the intuition that privacy means that the
adversary cannot significantly increasing its ability to conclude that an
entity is in the input dataset. We show that several recently proposed
privacy notions, including differential privacy, are instantiations of the
membership privacy framework, and that the framework provides a principled
approach to developing new privacy notions under which better utility can be
achieved than what is possible under differential privacy.