We propose new privacy attacks to infer attributes (eg, locations, occupations, and interests)
of online social network users. Our attacks leverage seemingly innocent user information …

We focus on the privacy-utility trade-off encountered by users who wish to disclose some
information to an analyst, that is correlated with their private data, in the hope of receiving …

Targeted advertising has transformed the marketing landscape for a wide variety of
businesses, by creating new opportunities for advertisers to reach prospective customers by …

Personalized recommendation is crucial to help users find pertinent information. It often
relies on a large collection of user data, in particular users' online activity (eg, tagging/rating …

We explore properties and applications of the principal inertia components (PICs) between
two discrete random variables X and Y. The PICs lie in the intersection of information and …

In a decentralized Internet of Things (IoT) network, a fusion center receives information from
multiple sensors to infer a public hypothesis of interest. To prevent the fusion center from …

We propose a practical methodology to protect a user's private data, when he wishes to
publicly release data that is correlated with his private data, to get some utility. Our approach …

We focus on the privacy-accuracy tradeoff encountered by a user who wishes to release
some data to an analyst, that is correlated with his private data, in the hope of receiving …

We investigate the problem of intentionally disclosing information about a set of
measurement points X (useful information), while guaranteeing that little or no information is …

We consider the problem of protecting individual user's location privacy at the trace-level
and study the privacy-utility trade-off, which has key applications in privacy-preserving …