Darakhshan J. Mir

DP-WHERE: Differentially private modeling of human mobility

Models of human mobility have broad applicability in urban planning, ecology, epidemiology, and other fields. Starting with Call Detail Records (CDRs) from a cellular telephone network that have gone through a straightforward anonymization procedure, the prior WHERE modeling approach produces synthetic CDRs for a synthetic population. The accuracy of WHERE has been validated against billions of location samples for hundreds of thousands of cell phones in the New York and Los Angeles metropolitan areas. In this paper, we introduce DP-WHERE, which modifies WHERE by adding controlled noise to achieve differential privacy, a strict definition of privacy that makes no assumptions about the power or background knowledge of a potential adversary. We also present experiments showing that the accuracy of DP-WHERE remains close to that of WHERE and of real CDRs. With this work, we aim to enable the creation and possible release of synthetic models that capture the mobility patterns of real metropolitan populations while preserving privacy.

Pan-private algorithms via statistics on sketches

Consider fully dynamic data, where we track data as it gets inserted and deleted. There are well developed notions of private data analyses with dynamic data, for example, using differential privacy. We want to go beyond privacy, and consider privacy together with security, formulated recently as pan-privacy by Dwork et al. (ICS 2010). Informally, pan-privacy preserves differential privacy while computing desired statistics on the data, even if the internal memory of the algorithm is compromised (say, by a malicious break-in or insider curiosity or by fiat by the government or law). We study pan-private algorithms for basic analyses, like estimating distinct count, moments, and heavy hitter count, with fully dynamic data. We present the first known pan-private algorithms for these problems in the fully dynamic model. Our algorithms rely on sketching techniques popular in streaming: in some cases, we add suitable noise to a previously known sketch, using a novel approach of calibrating noise to the underlying problem structure and the projection matrix of the sketch; in other cases, we maintain certain statistics on sketches; in yet others, we define novel sketches. We also present the first known lower bounds explicitly for pan privacy, showing our results to be nearly optimal for these problems. Our lower bounds are stronger than those implied by differential privacy or dynamic data streaming alone and hold even if unbounded memory and/or unbounded processing time are allowed. The lower bounds use a noisy decoding argument and exploit a connection between pan-private algorithms and data sanitization.

A Differentially Private Graph Estimator

We consider the problem of making graph databases such as social network structures available to researchers for knowledge discovery while providing privacy to the participating entities. We show that for a specific parametric graph model, the Kronecker graph model, one can construct an estimator of the true parameter in a way that both satisfies the rigorous requirements of differential privacy and is asymptotically efficient in the statistical sense. The estimator, which may then be published, defines a probability distribution on graphs. Sampling such a distribution yields a synthetic graph that mimics important properties of the original sensitive graph and, consequently, could be useful for knowledge discovery.

Differentially-private learning and information theory

Using results from PAC-Bayesian bounds in learning theory, we formulate differentially-private learning in an information theoretic framework. This, to our knowledge, is the first such treatment of this increasingly popular notion of data privacy. We examine differential privacy in the PAC-Bayesian framework and through such a treatment examine the relation between differentially-private learning and learning in a scenario where we seek to minimize the expected risk under mutual information constraints. We establish a connection between the exponential mechanism, which is the most general differentially private mechanism and the Gibbs estimator encountered in PAC-Bayesian bounds. We discover that the goal of finding a probability distribution that minimizes the so-called PAC-Bayesian bounds (under certain assumptions), leads to the Gibbs estimator which is differentially-private.

Information-Theoretic foundations of differential privacy

We examine the information-theoretic foundations of the increasingly popular notion of differential privacy. We establish a connection between differential private mechanisms and the rate-distortion framework. Additionally, we also show how differentially private distributions arise out of the application of the Maximum Entropy Principle. This helps us locate differential privacy within the wider framework of information-theory and helps formalize some intuitive aspects of our understanding of differential privacy.

Differential privacy: an exploration of the privacy-utility landscape

Facilitating use of sensitive data for research or commercial purposes, in a manner that preserves the privacy of participating entities, is an active area of study. Differential privacy is a popular, relatively recent, framework that formalizes data privacy. In this dissertation, I examine the often conflicting goals of privacy and utility within the framework of differential privacy. The contributions of this dissertation fall into two main categories: 1) We propose differentially private algorithms for several tasks that could potentially involve sensitive data, such as synthetic graph modeling, human mobility modeling using cellular phone data, regression, and computing statistics on online data. We study the tradeoff between privacy and utility for these analyses—theoretically in some cases, and experimentally in others. We show that for each of these tasks, both privacy and utility can be successfully achieved by considering a meaningful tradeoff between the two. 2) We also examine connections between information theory and differential privacy, demonstrating how differential privacy arises out of a tradeoff between information leakage and utility. We show that differentially private mechanisms arise out of minimizing the information leakage (measured using mutual information) under the constraint of achieving a given level of utility. Further, we establish a connection between differentially private learning and PAC-Bayesian bounds.

Related-Key Linear Cryptanalysis

A coding theory framework for related-key linear cryptanalytic attacks on block ciphers is presented. It treats linear cryptanalysis as communication over a low capacity channel, and a related key attack (RKA) as a concatenated code. It is used to show that an RKA, using n related keys generated from k independent ones, can improve the amortized cost - in number of plaintext-ciphertext pairs per key bit determined over that of k single key attacks, of any linear cryptanalysis, if k and n are large enough. The practical implications of this result are demonstrated through the design of an RKA, with k=5 and n=7, predicted to produce a 29% improvement for DES attacks that use an r-1 round approximation

How PhD students at research universities can prepare for a career at a liberal arts college (abstract only)

We will discuss how to better organize as graduate students and postdoctoral researchers seeking a career in liberal arts colleges (LACs). The BoF will bring together those who are interested in a career path to a LAC but do not have reliable advice and mentorship in their home departments and often turn out to be the only person in their department with such a career choice. Additionally, several people who have recently made a successful transition from graduate school to new faculty positions will attend the BoF.