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, in the hope of getting some utility. Our approach relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework,… (More)
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 some utility. We rely on a general privacy statistical inference framework, under which data is transformed before it is disclosed, according to a probabilistic privacy… (More)
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 relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework, data is… (More)
We introduce a property that we call Successive Description property for Slepian Wolf coding. We show that Monotone-Chain Polar Codes can be used to construct low-complexity codes that satisfy this property. We discuss applications of this property to network coding problems.
—Consider a multi-source network coding problem with correlated sources. While the fundamental limits are known, achieving them, in general, involves a computational burden due to the complex decoding process. Efficient solutions, on the other hand, are by large based on source and network coding separation, thus imposing strict topological constraints on… (More)
We study the problem of a user who has both public and private data, and wants to release the public data, e.g. to a recommendation service, yet simultaneously wants to protect his private data from being inferred via big data analytics. This problem has previously been formulated as a convex optimization problem with linear constraints where the objective… (More)
MIT Abstract—Consider two correlated sources X and Y generated from a joint distribution pX,Y. Their Gács-Körner Common Information, a measure of common information that exploits the combinatorial structure of the distribution pX,Y , leads to a source decomposition that exhibits the latent common parts in X and Y. Using this source decomposition we… (More)