We study differential privacy in a distributed setting where two parties would like to perform analysis of their joint data while preserving privacy for both datasets. Our results ...
Andrew McGregor, Ilya Mironov, Toniann Pitassi, Om...
We introduce a new practical mechanism for remote data storage with efficient access pattern privacy and correctness. A storage client can deploy this mechanism to issue encrypted...
We propose COP, a client-side system for protecting children’s online privacy and empowering parental control over children’s information disclosure with little manual effort. ...
The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-pu...
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transact...
Alexandre V. Evfimievski, Ramakrishnan Srikant, Ra...