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We propose a general statistical inference framework to capture the privacy threat incurred by a user that releases data to a passive but curious adversary, given utility constraints. We show that applying this general framework to the setting where the adversary uses the self-information cost function naturally leads to a non-asymptotic(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, 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 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 some utility. We rely on a general statistical inference framework, under which data is distorted before its release, according to a probabilistic privacy mechanism designed under(More)
In many monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses --- predicate sums --- in this context. Formally, we study the problem of estimating predicate sums <i>privately</i>, for sliding windows and other decay models. While we require accuracy(More)
This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4 536 891 ratings provided by 171 670 users on 23 974 movies, as well as the household groupings of a subset of the users. The test dataset comprises 5 450 ratings for which the user label is missing,(More)
We describe a noncooperative interference alignment (IA) technique which allows an opportunistic multiple input multiple output (MIMO) link (secondary) to harmlessly coexist with another MIMO link (primary) in the same frequency band. Assuming perfect channel knowledge at the primary receiver and transmitter, capacity is achieved by transmiting along the(More)
— We develop and analyze new cooperative strategies for ad hoc networks that are more spectrally efficient than classical DF cooperative protocols. Using analog network coding, our strategies preserve the practical half-duplex assumption but relax the orthogonality constraint. The introduction of interference due to non-orthogonality is mitigated thanks to(More)
A multihop relaying system is analyzed where data sent by a multi-antenna source is relayed by successive multi-antenna relays until it reaches a multi-antenna destination. Assuming correlated fading at each hop, each relay receives a faded version of the signal from the previous level, performs linear precoding and retransmits it to the next level. Using(More)
It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the user-provided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification is feasible, it opens the way to(More)