Umar Syed
I received a Ph.D. in Computer Science from Princeton University, where I was advised by Rob Schapire. I spent two years as a postdoctoral researcher at the University of Pennsylvania, hosted by Ben Taskar and Michael Kearns.
Authored Publications
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New regulations and increased awareness of data privacy have led to the deployment of new and more efficient differentially private mechanisms across public institutions and industries. Ensuring the correctness of these mechanisms is therefore crucial to ensure the proper protection of data. However, since differential privacy is a property of the mechanism itself, and not of an individual output, testing whether a mechanism is differentially private is not a trivial task. While ad hoc testing techniques exist under specific assumptions, no concerted effort has been made by the research community to develop a flexible and extendable tool for testing differentially private mechanisms. This paper introduces DP-Auditorium as a step advancing research in this direction. DP-Auditorium abstracts the problem of testing differential privacy into two steps: (1) measuring the distance between distributions, and (2) finding neighboring datasets where a mechanism generates output distributions maximizing such distance. From a technical point of view, we propose three new algorithms for evaluating the distance between distributions. While these algorithms are well-established in the statistics community, we provide new estimation guarantees that exploit the fact that we are only interested in verifying whether a mechanism is differentially private, and not in obtaining an exact estimate of the distance between two distributions. DP-Auditorium is easily extensible, as demonstrated in this paper by implementing a well-known approximate differential privacy testing algorithm into our library. We provide an extensive comparison to date of multiple testers across varying sample sizes and differential privacy parameters, demonstrating that there is no single tester that dominates all others, and that a combination of different techniques is required to ensure
proper testing of mechanisms.
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A lexicographic maximum of a set $X \subseteq R^n$ is a vector in $X$ whose smallest component is as large as possible, and subject to that requirement, whose second smallest component is as large as possible, and so on for the third smallest component, etc. Lexicographic maximization has numerous practical and theoretical applications, including fair resource allocation, analyzing the implicit regularization of learning algorithms, and characterizing refinements of game-theoretic equilibria. We prove that a minimizer in $X$ of the exponential loss function $L_c(x) = \sum_i \exp(-c x_i)$ converges to a lexicographic maximum of $X$ as $c \rightarrow \infty$, provided that $X$ is stable in the sense that a well-known iterative method for finding a lexicographic maximum of $X$ cannot be made to fail simply by reducing the required quality of each iterate by an arbitrarily tiny degree. Our result holds for both near and exact minimizers of the exponential loss, while earlier convergence results made much stronger assumptions about the set $X$ and only held for the exact minimizer. We are aware of no previous results showing a connection between the iterative method for computing a lexicographic maximum and exponential loss minimization. We show that every convex polytope is stable, but that there exist compact, convex sets that are not stable. We also provide the first analysis of the convergence rate of an exponential loss minimizer (near or exact) and discover a curious dichotomy: While the two smallest components of the vector converge to the lexicographically maximum values very quickly (at roughly the rate $(\log n)/c$), all other components can converge arbitrarily slowly.
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Assessing Web Fingerprinting Risk
Robert Busa-Fekete
Antonio Sartori
Proceedings of the ACM Web Conference (WWW 2024)
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Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused by malign actors to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled.
Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. Earlier studies that estimated the entropy of Web APIs were based on data from a single website or were limited to an extremely small sample of clients. They also analyzed each Web API separately and then summed their entropies to quantify overall fingerprinting risk, an approach that can lead to gross overestimates.
We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web API function calls reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting.
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We study differentially private mechanisms for sharing training data in machine learning settings. Our goal is to enable learning of an accurate predictive model while protecting the privacy of each user’s label. Previous work established privacy guarantees that assumed the features are public and given exogenously, a setting known as label differential privacy. In some scenarios, this can be a strong assumption that removes the interplay between features and labels from the privacy analysis. We relax this approach and instead assume the features are drawn from a distribution that depends on the private labels. We first show that simply adding noise to the label, as in previous work, can lead to an arbitrarily weak privacy guarantee, and also present methods for estimating this privacy loss from data. We then present a new mechanism that replaces some training examples with synthetically generated data, and show that our mechanism has a much better privacy-utility tradeoff if the synthetic data is realistic, in a certain quantifiable sense. Finally, we empirically validate our theoretical analysis.
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Modern statistical estimation is often performed in a distributed setting where each sample
belongs to a single user who shares their data with a central server. Users are typically concerned with preserving the privacy of their samples, and also with minimizing the amount of
data they must transmit to the server. We give improved private and communication-efficient
algorithms for estimating several popular measures of the entropy of a distribution. All of our
algorithms have constant communication cost and satisfy local differential privacy. For a joint
distribution over many variables whose conditional independence is given by a tree, we describe
algorithms for estimating Shannon entropy that require a number of samples that is linear in
the number of variables, compared to the quadratic sample complexity of prior work. We also
describe an algorithm for estimating Gini entropy whose sample complexity has no dependence on the support size of the distribution and can be implemented using a single round of concurrent communication between the users and the server. In contrast, the previously best-known algorithm has high communication cost and requires the server to facilitate interaction between the users. Finally, we describe an algorithm for estimating collision entropy that generalizes the best known algorithm to the private and communication-efficient setting.
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We present new mechanisms for label differential privacy, a relaxation of differentially private
machine learning that only protects the privacy of the labels in the training set. Our mechanisms
cluster the examples in the training set using their (non-private) feature vectors, randomly
re-sample each label from examples in the same cluster, and output a training set with noisy labels as well as a modified version of the true loss function. We prove that when the clusters are both large and high-quality, the model that minimizes the modified loss on the noisy training set converges to small excess risk at a rate that is comparable to the rate for non-private learning. We describe both a centralized mechanism in which the entire training set is stored by a trusted curator, and a distributed mechanism where each user stores a single labeled example and replaces her label with the label of a randomly selected user from the same cluster. We also
describe a learning problem in which large clusters are necessary to achieve both strong privacy and either good precision or good recall. Our experiments show that randomizing the labels within each cluster significantly improves the privacy vs. accuracy trade-off compared to applying uniform randomized response to the labels, and also compared to learning a model via DP-SGD.
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We study the problem of differentially private optimization with linear constraints when the right-hand side of the constraints depends on private data. This type of problem appears in many applications, especially resource allocation. Previous research provided solutions that retained privacy but sometimes violated the constraints. In many settings, however, the constraints cannot be violated under any circumstances. To address this hard requirement, we present an algorithm that releases a nearly-optimal solution satisfying the constraints with probability 1. We also prove a lower bound demonstrating that the difference between the objective value of our algorithm’s solution and the optimal solution is tight up to logarithmic factors among all differentially private algorithms. We conclude with experiments demonstrating that our algorithm can achieve nearly optimal performance while preserving privacy.
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We study the cost sharing problem for cooperative games in situations where the cost function C is not available via oracle queries, but must instead be derived from data, represented as tuples (S, C(S)), for different subsets S of players. We formalize this approach, which we call STATISTICAL COST SHARING, and consider the computation of the core and the Shapley value, when the tuples are drawn from some distribution D.
Previous work by Balcan et al. [8] in this setting showed how to compute cost shares that satisfy the core property with high probability for limited classes of functions. We expand on their work and give an algorithm that computes such cost shares for any function with a non-empty core. We complement these results by proving an inapproximability lower bound for a weaker relaxation.
We then turn our attention to the Shapley value. We first show that when cost functions come from the family of submodular functions with bounded curvature, k, the Shapley value can be approximated from samples up to a \sqrt{1 − k} factor, and that the bound is tight. We then define statistical analogues of the Shapley axioms, and derive a notion of statistical Shapley value. We show that these can always be approximated arbitrarily well for general functions over any distribution D.
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Learning mobile phone battery consumptions
Ashish Sharma
Paul Eastham
Workshop on On Device Intelligence (2016)
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We introduce a novel, data-driven way for predicting battery consumption of apps. The state-of-the-art models used to blame battery consumption on apps are based on micro-benchmark experiments. These experiments are carried out on controlled setups where one can measure how much battery is consumed by each internal resource (CPU, bluetooth, WiFi...). The battery blame allocated to an app is simply the sum of the blames of the resources consumed by the app. We argue that this type of models do not capture the way phones work "in the wild" and propose instead to train a regression model using data collected from logs. We show that this type of learning is correct in the sense that under some assumptions, we can recover the true battery discharge rate of each component. We present experimental results where we consistently do better predictions than a model trained on micro-benchmarks.
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Where to sell: Simulating auctions from learning algorithms
Hamid Nazerzadeh
Proceedings of the Seventeenth ACM Conference on Economics and Computation (EC2016)
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Ad Exchange platforms connect online publishers and advertisers and facilitate selling billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit maximizing exchange to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not possible due to technological and other practical considerations. The publisher needs to send each impression to one of the exchanges with an asking price. We model the problem as a variation of multi-armed bandits where exchanges (arms) can behave strategically in order to maximizes their own profit. We propose mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.
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