• Publications
  • Influence
The Algorithmic Foundations of Differential Privacy
TLDR
The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example.
A learning theory approach to non-interactive database privacy
TLDR
A new notion of data privacy is introduced, which is called distributional privacy, and it is shown that it is strictly stronger than the prevailing privacy notion, differential privacy.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this article, we seek to clarify
Preserving Statistical Validity in Adaptive Data Analysis
TLDR
It is shown that, surprisingly, there is a way to estimate an exponential in n number of expectations accurately even if the functions are chosen adaptively, and this gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
TLDR
It is proved that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses.
Generalization in Adaptive Data Analysis and Holdout Reuse
TLDR
A simple and practical method for reusing a holdout set to validate the accuracy of hypotheses produced by a learning algorithm operating on a training set and it is shown that a simple approach based on description length can also be used to give guarantees of statistical validity in adaptive settings.
Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms
TLDR
These ideas are extended to give a simple greedy-based constant factor algorithms for non-monotone submodular maximization subject to a knapsack constraint, and for (online) secretary setting subject to uniform matroid or a partition matroid constraint.
Selling privacy at auction
TLDR
It is shown that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy, and modeling it correctly is one of the many exciting directions for future work.
Differentially private combinatorial optimization
TLDR
It is shown that many such problems indeed have good approximation algorithms that preserve differential privacy, even in cases where it is impossible to preserve cryptographic definitions of privacy while computing any non-trivial approximation to even the value of an optimal solution, let alone the entire solution.
Regret minimization and the price of total anarchy
TLDR
It is proved that despite the weakened assumptions, in several broad classes of games, this "price of total anarchy" matches the Nash price of anarchy, even though play may never converge to Nash equilibrium.
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