Adam D. Smith

ORCID

Publication Activity (10 Years)

Years Active: 1999-2023
Publications (10 Years): 79

Top Venues

CoRR

NeurIPS

IACR Cryptol. ePrint Arch.

FOCS

Massachusetts Institute of Technology, Cambridge, MA, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, MA, USA

PHD

Publications

Palak Jain, Iden Kalemaj, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation. NeurIPS (2023)
Iden Kalemaj, Sofya Raskhodnikova, Adam D. Smith, Charalampos E. Tsourakakis
Node-Differentially Private Estimation of the Number of Connected Components. PODS (2023)
Aloni Cohen, Adam D. Smith, Marika Swanberg, Prashant Nalini Vasudevan
Control, Confidentiality, and the Right to be Forgotten. CCS (2023)
Palak Jain, Iden Kalemaj, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith
Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation. CoRR (2023)
Talya Eden, Quanquan C. Liu, Sofya Raskhodnikova, Adam D. Smith
Triangle Counting with Local Edge Differential Privacy. CoRR (2023)
Gavin Brown, Samuel B. Hopkins, Adam D. Smith
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions. CoRR (2023)
Iden Kalemaj, Sofya Raskhodnikova, Adam D. Smith, Charalampos E. Tsourakakis
Node-Differentially Private Estimation of the Number of Connected Components. CoRR (2023)
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith
The Price of Differential Privacy under Continual Observation. ICML (2023)
Talya Eden, Quanquan C. Liu, Sofya Raskhodnikova, Adam D. Smith
Triangle Counting with Local Edge Differential Privacy. ICALP (2023)
Adam D. Smith, Abhradeep Thakurta
Fully Adaptive Composition for Gaussian Differential Privacy. CoRR (2022)
Audra McMillan, Adam D. Smith, Jonathan R. Ullman
Instance-Optimal Differentially Private Estimation. CoRR (2022)
Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. NeurIPS (2022)
Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. 2022 (2) (2022)
Gavin Brown, Mark Bun, Adam D. Smith
Strong Memory Lower Bounds for Learning Natural Models. CoRR (2022)
Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg
Differentially Private Sampling from Distributions. CoRR (2022)
Gavin Brown, Mark Bun, Adam D. Smith
Strong Memory Lower Bounds for Learning Natural Models. COLT (2022)
Aloni Cohen, Adam D. Smith, Marika Swanberg, Prashant Nalini Vasudevan
Control, Confidentiality, and the Right to be Forgotten. CoRR (2022)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. SIAM J. Comput. 50 (3) (2021)
Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg
Differentially Private Sampling from Distributions. NeurIPS (2021)
Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. CoRR (2021)
Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta
Differentially Private Model Personalization. NeurIPS (2021)
Leonid Reyzin, Adam D. Smith, Sophia Yakoubov
Turning HATE into LOVE: Compact Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. CSCML (2021)
Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar
When is memorization of irrelevant training data necessary for high-accuracy learning? STOC (2021)
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith
The Price of Differential Privacy under Continual Observation. CoRR (2021)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. IEEE Symposium on Security and Privacy (2021)
Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. NeurIPS (2021)
Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith
Non-parametric Differentially Private Confidence Intervals for the Median. CoRR (2021)
Ran Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam D. Smith
Reusable Fuzzy Extractors for Low-Entropy Distributions. J. Cryptol. 34 (1) (2021)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11 (1) (2021)
Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. CoRR (2020)
Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. J. Mach. Learn. Res. 21 (2020)
Adam D. Smith, Shuang Song, Abhradeep Thakurta
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. NeurIPS (2020)
Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan
Differentially Private Simple Linear Regression. CoRR (2020)
Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? CoRR (2020)
Benjamin Fuller, Leonid Reyzin, Adam D. Smith
When Are Fuzzy Extractors Possible? IEEE Trans. Inf. Theory 66 (8) (2020)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS (2020)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Shuffling. EUROCRYPT (1) (2019)
Di Wang, Adam D. Smith, Jinhui Xu
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. ALT (2019)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Shuffling. IACR Cryptol. ePrint Arch. 2019 (2019)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman
Manipulation Attacks in Local Differential Privacy. CoRR (2019)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR (2019)
Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, Adam D. Smith
From Soft Classifiers to Hard Decisions: How fair can we be? FAT (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The structure of optimal private tests for simple hypotheses. STOC (2019)
Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR (2018)
Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS (2018)
Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
The Limits of Post-Selection Generalization. NeurIPS (2018)
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
The Limits of Post-Selection Generalization. CoRR (2018)
Leonid Reyzin, Adam D. Smith, Sophia Yakoubov
Turning HATE Into LOVE: Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. IACR Cryptol. ePrint Arch. 2018 (2018)
Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik
Private Algorithms Can Always Be Extended. CoRR (2018)
Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation. CoRR (2018)
Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev
Distributed Differential Privacy via Mixnets. CoRR (2018)
Di Wang, Adam D. Smith, Jinhui Xu
Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation. CoRR (2018)
Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation. FOCS (2018)
Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, Adam D. Smith
From Soft Classifiers to Hard Decisions: How fair can we be? CoRR (2018)
Eike Kiltz, Adam O'Neill, Adam D. Smith
Instantiability of RSA-OAEP Under Chosen-Plaintext Attack. J. Cryptol. 30 (3) (2017)
Adam D. Smith
Information, Privacy and Stability in Adaptive Data Analysis. CoRR (2017)
Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay
Is Interaction Necessary for Distributed Private Learning? IEEE Symposium on Security and Privacy (2017)
Venkatesan Guruswami, Adam D. Smith
Optimal Rate Code Constructions for Computationally Simple Channels. J. ACM 63 (4) (2016)
Sofya Raskhodnikova, Adam D. Smith
Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism. FOCS (2016)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic stability for adaptive data analysis. STOC (2016)
Ran Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam D. Smith
Reusable Fuzzy Extractors for Low-Entropy Distributions. EUROCRYPT (1) (2016)
Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR (2016)
Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS (2016)
Benjamin Fuller, Leonid Reyzin, Adam D. Smith
When Are Fuzzy Extractors Possible? ASIACRYPT (1) (2016)
Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith
Calibrating Noise to Sensitivity in Private Data Analysis. J. Priv. Confidentiality 7 (3) (2016)
Audra McMillan, Adam D. Smith
When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? CoRR (2016)
Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
Privacy-Preserving Public Information for Sequential Games. ITCS (2015)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. CoRR (2015)
Raef Bassily, Adam D. Smith
Local, Private, Efficient Protocols for Succinct Histograms. STOC (2015)
Adam D. Smith, Ye Zhang
On the Regularity of Lossy RSA: Improved Bounds and Applications to Padding-Based Encryption. IACR Cryptol. ePrint Arch. 2015 (2015)
Sofya Raskhodnikova, Adam D. Smith
Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions. CoRR (2015)
Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan
Robust Traceability from Trace Amounts. FOCS (2015)
Christian Borgs, Jennifer T. Chayes, Adam D. Smith
Private Graphon Estimation for Sparse Graphs. CoRR (2015)
Raef Bassily, Adam D. Smith
Local, Private, Efficient Protocols for Succinct Histograms. CoRR (2015)
Sean Hallgren, Adam D. Smith, Fang Song
Classical Cryptographic Protocols in a Quantum World. IACR Cryptol. ePrint Arch. 2015 (2015)
Adam D. Smith, Ye Zhang
On the Regularity of Lossy RSA - Improved Bounds and Applications to Padding-Based Encryption. TCC (1) (2015)
Christian Borgs, Jennifer T. Chayes, Adam D. Smith
Private Graphon Estimation for Sparse Graphs. NIPS (2015)
Sean Hallgren, Adam D. Smith, Fang Song
Classical Cryptographic Protocols in a Quantum World. CoRR (2015)
Craig Gentry, Jens Groth, Yuval Ishai, Chris Peikert, Amit Sahai, Adam D. Smith
Using Fully Homomorphic Hybrid Encryption to Minimize Non-interative Zero-Knowledge Proofs. J. Cryptol. 28 (4) (2015)
Benjamin Fuller, Adam D. Smith, Leonid Reyzin
Where are Fuzzy Extractors Possible? IACR Cryptol. ePrint Arch. 2014 (2014)
Vishesh Karwa, Sofya Raskhodnikova, Adam D. Smith, Grigory Yaroslavtsev
Private Analysis of Graph Structure. ACM Trans. Database Syst. 39 (3) (2014)
Raef Bassily, Adam D. Smith
Causal Erasure Channels. SODA (2014)
Raef Bassily, Adam D. Smith
Causal Erasure Channels. CoRR (2014)
Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
Privacy-Preserving Public Information for Sequential Games. CoRR (2014)
Raef Bassily, Adam D. Smith, Abhradeep Thakurta
Private Empirical Risk Minimization, Revisited. CoRR (2014)
Shiva Prasad Kasiviswanathan, Adam D. Smith
On the 'Semantics' of Differential Privacy: A Bayesian Formulation. J. Priv. Confidentiality 6 (1) (2014)
Raef Bassily, Adam D. Smith, Abhradeep Thakurta
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. FOCS (2014)
Abhradeep Guha Thakurta, Adam D. Smith
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. NIPS (2013)
Adam D. Smith, Ye Zhang
Near-linear time, Leakage-resilient Key Evolution Schemes from Expander Graphs. IACR Cryptol. ePrint Arch. 2013 (2013)
Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith
The Power of Linear Reconstruction Attacks. SODA (2013)
Raef Bassily, Adam Groce, Jonathan Katz, Adam D. Smith
Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy. FOCS (2013)
Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Adam D. Smith
Sublinear Algorithms for Approximating String Compressibility. Algorithmica 65 (3) (2013)
Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith
Analyzing Graphs with Node Differential Privacy. TCC (2013)
Abhradeep Thakurta, Adam D. Smith
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. COLT (2013)
Mark Lewko, Adam O'Neill, Adam D. Smith
Regularity of Lossy RSA on Subdomains and Its Applications. EUROCRYPT (2013)
Yevgeniy Dodis, Bhavana Kanukurthi, Jonathan Katz, Leonid Reyzin, Adam D. Smith
Robust Fuzzy Extractors and Authenticated Key Agreement From Close Secrets. IEEE Trans. Inf. Theory 58 (9) (2012)
Daniel Kifer, Adam D. Smith, Abhradeep Thakurta
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. COLT (2012)
Aleksandra B. Slavkovic, Adam D. Smith
Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy. J. Priv. Confidentiality 4 (1) (2012)
Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith
The Power of Linear Reconstruction Attacks CoRR (2012)
Adam D. Smith
Privacy-preserving statistical estimation with optimal convergence rates. STOC (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Adam D. Smith