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Adam Smith

Pennsylvania State University
Weizmann Institute of Science
Massachusetts Institute of Technology

294

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23898

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Manipulation Attacks in Local Differential Privacy

2021 S&P | IEEE Symposium on Security and Privacy
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Differentially Private Sampling from Distributions

2021 | Annual Conference on Neural Information Processing Systems
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Reusable Fuzzy Extractors for Low-Entropy Distributions

2021 Journal of Cryptology
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The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space

2020 | Annual Conference on Neural Information Processing Systems
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The structure of optimal private tests for simple hypotheses

2019 | ACM Symposium on Theory of Computing
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Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.

2019 ALT | Algorithmic Learning Theory
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The Limits of Post-Selection Generalization

2018 | Annual Conference on Neural Information Processing Systems
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When is non-trivial estimation possible for graphons and stochastic block models?‡

2018 arXiv: Statistics Theory
- Audra McMillan
- Adam Smith
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Private Algorithms Can Always Be Extended.

2018 arXiv: Statistics Theory
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Distributed Differential Privacy via Mixnets.

2018 arXiv: Cryptography and Security
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Citations (24)