Salman Salamatian

How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data

We propose a practical methodology to protect a users 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, data is distorted before it is released, according to a privacy-preserving probabilistic mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We address a practical challenge encountered when applying this theoretical framework to real world data: the optimization may become untractable and face scalability issues when data assumes values in large size alphabets, or is high dimensional. Our work makes two major contributions. We first reduce the optimization size by introducing a quantization step, and show how to generate privacy mappings under quantization. Second, we evaluate our method on a dataset showing correlations between political views and TV viewing habits, and demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g. recommendations.

From the Information Bottleneck to the Privacy Funnel

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 mapping. We show that when the log-loss is introduced in this framework in both the privacy metric and the distortion metric, the privacy leakage and the utility constraint can be reduced to the mutual information between private data and disclosed data, and between non-private data and disclosed data respectively. We justify the relevance and generality of the privacy metric under the log-loss by proving that the inference threat under any bounded cost function can be upperbounded by an explicit function of the mutual information between private data and disclosed data. We then show that the privacy-utility tradeoff under the log-loss can be cast as the non-convex Privacy Funnel optimization, and we leverage its connection to the Information Bottleneck, to provide a greedy algorithm that is locally optimal. We evaluate its performance on the US census dataset. Finally, we characterize the optimal privacy mapping for the Gaussian Privacy Funnel.

Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy

We propose a practical methodology to protect a users 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 distorted before it is released, according to a probabilistic privacy mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We address practical challenges encountered when applying this theoretical framework to real world data. On one hand, the design of optimal privacy mappings requires knowledge of the prior distribution linking private data and data to be released, which is often unavailable in practice. On the other hand, the optimization may become untractable when data assumes values in large size alphabets, or is high dimensional. Our work makes three major contributions. First, we provide bounds on the impact of a mismatched prior on the privacy-utility tradeoff. Second, we show how to reduce the optimization size by introducing a quantization step, and how to generate privacy mappings under quantization. Third, we evaluate our method on two datasets, including a new dataset that we collected, showing correlations between political convictions and TV viewing habits. We demonstrate that good privacy properties can be achieved with limited distortion so as not to undermine the original purpose of the publicly released data, e.g., recommendations.

Guessing with limited memory

Suppose that we wish to guess the realization x of a discrete random variable X taking values in a finite set, by asking sequential questions of the form “Is X is equal to x?” exhausting the elements of X until the answer is Yes. [1, 2]. If the distribution of X is known to the guesser, and the guesser has memory of his previous has memory of his previous queries then the best strategy is to guess in decreasing order of probabilities. In this paper, we consider the problem of a memoryless guesser, namely, each new guess is independent of the previous guesses. We consider also the scenario of a guesser with a bounded number of guesses. For both cases we derive the optimal guessing strategies, and show new connections to Rényi entropy.

A Successive Description property of Monotone-Chain Polar Codes for Slepian-Wolf coding

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.

Centralized vs decentralized multi-agent guesswork

We study a notion of guesswork, where multiple agents intend to launch a coordinated brute-force attack to find a single binary secret string, and each agent has access to side information generated through either a BEC or a BSC. The average number of trials required to find the secret string grows exponentially with the length of the string, and the rate of the growth is called the guesswork exponent. We compute the guesswork exponent for several multi-agent attacks. We show that a multi-agent attack reduces the guesswork exponent compared to a single agent, even when the agents do not exchange information to coordinate their attack, and try to individually guess the secret string using a predetermined scheme in a decentralized fashion. Further, we show that the guesswork exponent of two agents who do coordinate their attack is strictly smaller than that of any finite number of agents individually performing decentralized guesswork.

Gaussian ISI channels with mismatch

This paper considers the problem of channel coding over Gaussian intersymbol interference (ISI) channels with a given (possibly suboptimal) metric decoding rule. Specifically, it is assumed that the mismatched decoder has incorrect knowledge of the ISI coefficients (or, the impulse response function). The mismatch capacity is the highest achievable rate for a given decoding rule. Unfortunately, existing lower bounds to the mismatch capacity for multi-letter channels and decoding metrics (or, channels and decoding metrics with memory), as in our model, are presented only in the form of multi-letter expressions, and thus cannot be calculated in practice. In this paper, we derive a computable single-letter lower bound to the mismatch capacity, and discuss some implications of our results.