Multiparty computation unconditionally secure against Q^2 adversary structures - Adam D. Smith 001; Anton Stiglic (CoRR, 1999)

Mutually Independent Commitments. - Moses D. Liskov; Anna Lysyanskaya; Silvio Micali; Leonid Reyzin; Adam D. Smith 001 (ASIACRYPT, 2001)

On Perfect and Adaptive Security in Exposure-Resilient Cryptography. - Yevgeniy Dodis; Amit Sahai; Adam D. Smith 001 (EUROCRYPT, 2001)

Efficient and Non-Interactive Non-Malleable Commitment. - Giovanni Di Crescenzo; Jonathan Katz; Rafail Ostrovsky; Adam D. Smith 001 (IACR Cryptol. ePrint Arch., 2001)

Extracting Quantum Entanglement. - Andris Ambainis; Adam D. Smith 001; Ke Yang 005 (CCC, 2002)

Detectable byzantine agreement secure against faulty majorities. - Matthias Fitzi; Daniel Gottesman; Martin Hirt; Thomas Holenstein; Adam D. Smith 001 (PODC, 2002)

Secure multi-party quantum computation. - Claude Crépeau; Daniel Gottesman; Adam D. Smith 001 (STOC, 2002)

Authentication of Quantum Messages. - Howard Barnum; Claude Crépeau; Daniel Gottesman; Adam D. Smith 001; Alain Tapp (IACR Cryptol. ePrint Arch., 2002)

Round Efficiency of Multi-party Computation with a Dishonest Majority. - Jonathan Katz; Rafail Ostrovsky; Adam D. Smith 001 (EUROCRYPT, 2003)

Lower bounds for collusion-secure fingerprinting. - Chris Peikert; Abhi Shelat; Adam D. Smith 001 (SODA, 2003)

Small Pseudo-random Families of Matrices: Derandomizing Approximate Quantum Encryption. - Andris Ambainis; Adam D. Smith 001 (APPROX-RANDOM, 2004)

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. - Yevgeniy Dodis; Leonid Reyzin; Adam D. Smith 001 (EUROCRYPT, 2004)

List-Decoding of Linear Functions and Analysis of a Two-Round Zero-Knowledge Argument. - Cynthia Dwork; Ronen Shaltiel; Adam D. Smith 001; Luca Trevisan 001 (TCC, 2004)

Entropic Security and the Encryption of High Entropy Messages. - Yevgeniy Dodis; Adam D. Smith 001 (IACR Cryptol. ePrint Arch., 2004)

Efficient Consistency Proofs for Generalized Queries on a Committed Database. - Rafail Ostrovsky; Charles Rackoff; Adam D. Smith 001 (IACR Cryptol. ePrint Arch., 2004)

Maintaining secrecy when information leakage is unavoidable. - Adam D. Smith 001 (2004)

Secure Remote Authentication Using Biometric Data. - Xavier Boyen; Yevgeniy Dodis; Jonathan Katz; Rafail Ostrovsky; Adam D. Smith 001 (EUROCRYPT, 2005)

Approximate Quantum Error-Correcting Codes and Secret Sharing Schemes. - Claude Crépeau; Daniel Gottesman; Adam D. Smith 001 (EUROCRYPT, 2005)

Correcting errors without leaking partial information. - Yevgeniy Dodis; Adam D. Smith 001 (STOC, 2005)

Toward Privacy in Public Databases. - Shuchi Chawla 001; Cynthia Dwork; Frank McSherry; Adam D. Smith 001; Hoeteck Wee (TCC, 2005)

Sublinear Algorithms for Approximating String Compressibility and the Distribution Support Size - Sofya Raskhodnikova; Dana Ron; Ronitt Rubinfeld; Amir Shpilka; Adam D. Smith 001 (Electron. Colloquium Comput. Complex., 2005)

Robust Fuzzy Extractors and Authenticated Key Agreement from Close Secrets. - Yevgeniy Dodis; Jonathan Katz; Leonid Reyzin; Adam D. Smith 001 (CRYPTO, 2006)

Secure Multiparty Quantum Computation with (Only) a Strict Honest Majority. - Michael Ben-Or; Claude Crépeau; Daniel Gottesman; Avinatan Hassidim; Adam D. Smith 001 (FOCS, 2006)

A Note on Adaptivity in Testing Properties of Bounded Degree Graphs. - Sofya Raskhodnikova; Adam D. Smith 001 (Electron. Colloquium Comput. Complex., 2006)

Analyzing the HB and HB+ Protocols in the "Large Error" Case. - Jonathan Katz; Adam D. Smith 001 (IACR Cryptol. ePrint Arch., 2006)

Scrambling Adversarial Errors Using Few Random Bits, Optimal Information Reconciliation, and Better Private Codes. - Adam D. Smith 001 (IACR Cryptol. ePrint Arch., 2006)

Smooth sensitivity and sampling in private data analysis. - Kobbi Nissim; Sofya Raskhodnikova; Adam D. Smith 001 (STOC, 2007)

Defending Against Attacks on Main Memory Persistence. - William Enck; Kevin R. B. Butler; Thomas Richardson; Patrick D. McDaniel; Adam D. Smith 001 (ACSAC, 2008)

Scalable Multiparty Computation with Nearly Optimal Work and Resilience. - Ivan Damgård; Yuval Ishai; Mikkel Krøigaard; Jesper Buus Nielsen; Adam D. Smith 001 (CRYPTO, 2008)

Efficient Two Party and Multi Party Computation Against Covert Adversaries. - Vipul Goyal; Payman Mohassel; Adam D. Smith 001 (EUROCRYPT, 2008)

Composition attacks and auxiliary information in data privacy. - Srivatsava Ranjit Ganta; Shiva Prasad Kasiviswanathan; Adam D. Smith 001 (KDD, 2008)

A Note on Differential Privacy: Defining Resistance to Arbitrary Side Information - Shiva Prasad Kasiviswanathan; Adam D. Smith 001 (CoRR, 2008)

Efficient, Differentially Private Point Estimators - Adam D. Smith 001 (CoRR, 2008)

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. - Yevgeniy Dodis; Rafail Ostrovsky; Leonid Reyzin; Adam D. Smith 001 (SIAM J. Comput., 2008)

Tight Bounds for Unconditional Authentication Protocols in the Manual Channel and Shared Key Models. - Moni Naor; Gil Segev 001; Adam D. Smith 001 (IEEE Trans. Inf. Theory, 2008)

Asymptotically Optimal and Private Statistical Estimation. - Adam D. Smith 001 (CANS, 2009)

What Can Cryptography Do for Coding Theory? - Adam D. Smith 001 (ICITS, 2009)

Composability and On-Line Deniability of Authentication. - Yevgeniy Dodis; Jonathan Katz; Adam D. Smith 001; Shabsi Walfish (TCC, 2009)

Strong Lower Bounds for Approximating Distribution Support Size and the Distinct Elements Problem. - Sofya Raskhodnikova; Dana Ron; Amir Shpilka; Adam D. Smith 001 (SIAM J. Comput., 2009)

Discovering frequent patterns in sensitive data. - Raghav Bhaskar; Srivatsan Laxman; Adam D. Smith 001; Abhradeep Thakurta (KDD, 2010)

The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. - Shiva Prasad Kasiviswanathan; Mark Rudelson; Adam D. Smith 001; Jonathan R. Ullman (STOC, 2010)

Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate. - Venkatesan Guruswami; Adam D. Smith 001 (Electron. Colloquium Comput. Complex., 2010)

Parallel and Concurrent Security of the HB and HB+ Protocols. - Jonathan Katz; Ji Sun Shin; Adam D. Smith 001 (J. Cryptol., 2010)

Differential Privacy for Statistics: What we Know and What we Want to Learn. - Cynthia Dwork; Adam D. Smith 001 (J. Priv. Confidentiality, 2010)

Privacy-preserving statistical estimation with optimal convergence rates. - Adam D. Smith 001 (STOC, 2011)

Private Analysis of Graph Structure. - Vishesh Karwa; Sofya Raskhodnikova; Adam D. Smith 001; Grigory Yaroslavtsev (Proc. VLDB Endow., 2011)

What Can We Learn Privately? - Shiva Prasad Kasiviswanathan; Homin K. Lee; Kobbi Nissim; Sofya Raskhodnikova; Adam D. Smith 001 (SIAM J. Comput., 2011)

Leftover Hashing Against Quantum Side Information. - Marco Tomamichel; Christian Schaffner; Adam D. Smith 001; Renato Renner (IEEE Trans. Inf. Theory, 2011)

Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. - Daniel Kifer; Adam D. Smith 001; Abhradeep Thakurta (COLT, 2012)

Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy. - Aleksandra B. Slavkovic; Adam D. Smith 001 (J. Priv. Confidentiality, 2012)

Robust Fuzzy Extractors and Authenticated Key Agreement From Close Secrets. - Yevgeniy Dodis; Bhavana Kanukurthi; Jonathan Katz; Leonid Reyzin; Adam D. Smith 001 (IEEE Trans. Inf. Theory, 2012)

Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. - Abhradeep Thakurta; Adam D. Smith 001 (COLT, 2013)

Regularity of Lossy RSA on Subdomains and Its Applications. - Mark Lewko; Adam O'Neill; Adam D. Smith 001 (EUROCRYPT, 2013)

Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy. - Raef Bassily; Adam Groce; Jonathan Katz; Adam D. Smith 001 (FOCS, 2013)

(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. - Abhradeep Guha Thakurta; Adam D. Smith 001 (NIPS, 2013)

The Power of Linear Reconstruction Attacks. - Shiva Prasad Kasiviswanathan; Mark Rudelson; Adam D. Smith 001 (SODA, 2013)

Analyzing Graphs with Node Differential Privacy. - Shiva Prasad Kasiviswanathan; Kobbi Nissim; Sofya Raskhodnikova; Adam D. Smith 001 (TCC, 2013)

Sublinear Algorithms for Approximating String Compressibility. - Sofya Raskhodnikova; Dana Ron; Ronitt Rubinfeld; Adam D. Smith 001 (Algorithmica, 2013)

Near-linear time, Leakage-resilient Key Evolution Schemes from Expander Graphs. - Adam D. Smith 001; Ye Zhang 001 (IACR Cryptol. ePrint Arch., 2013)

Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. - Raef Bassily; Adam D. Smith 001; Abhradeep Thakurta (FOCS, 2014)

Causal Erasure Channels. - Raef Bassily; Adam D. Smith 001 (SODA, 2014)

Private Empirical Risk Minimization, Revisited. - Raef Bassily; Adam D. Smith 001; Abhradeep Thakurta (CoRR, 2014)

Where are Fuzzy Extractors Possible? - Benjamin Fuller 001; Adam D. Smith 001; Leonid Reyzin (IACR Cryptol. ePrint Arch., 2014)

On the 'Semantics' of Differential Privacy: A Bayesian Formulation. - Shiva Prasad Kasiviswanathan; Adam D. Smith 001 (J. Priv. Confidentiality, 2014)

Robust Traceability from Trace Amounts. - Cynthia Dwork; Adam D. Smith 001; Thomas Steinke 002; Jonathan R. Ullman; Salil P. Vadhan (FOCS, 2015)

Privacy-Preserving Public Information for Sequential Games. - Avrim Blum; Jamie Morgenstern; Ankit Sharma 001; Adam D. Smith 001 (ITCS, 2015)

Private Graphon Estimation for Sparse Graphs. - Christian Borgs; Jennifer T. Chayes; Adam D. Smith 001 (NIPS, 2015)

Local, Private, Efficient Protocols for Succinct Histograms. - Raef Bassily; Adam D. Smith 001 (STOC, 2015)

Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions. - Sofya Raskhodnikova; Adam D. Smith 001 (CoRR, 2015)

Classical Cryptographic Protocols in a Quantum World. - Sean Hallgren; Adam D. Smith 001; Fang Song 001 (IACR Cryptol. ePrint Arch., 2015)

On the Regularity of Lossy RSA: Improved Bounds and Applications to Padding-Based Encryption. - Adam D. Smith 001; Ye Zhang 001 (IACR Cryptol. ePrint Arch., 2015)

Using Fully Homomorphic Hybrid Encryption to Minimize Non-interative Zero-Knowledge Proofs. - Craig Gentry; Jens Groth; Yuval Ishai; Chris Peikert; Amit Sahai; Adam D. Smith 001 (J. Cryptol., 2015)

Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism. - Sofya Raskhodnikova; Adam D. Smith 001 (FOCS, 2016)

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. - Ryan M. Rogers; Aaron Roth 001; Adam D. Smith 001; Om Thakkar 001 (FOCS, 2016)

When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? - Audra McMillan; Adam D. Smith 001 (CoRR, 2016)

Optimal Rate Code Constructions for Computationally Simple Channels. - Venkatesan Guruswami; Adam D. Smith 001 (J. ACM, 2016)

Calibrating Noise to Sensitivity in Private Data Analysis. - Cynthia Dwork; Frank McSherry; Kobbi Nissim; Adam D. Smith 001 (J. Priv. Confidentiality, 2016)

Differentially Private Analysis of Graphs. - Sofya Raskhodnikova; Adam D. Smith 001 (Encyclopedia of Algorithms, 2016)

Is Interaction Necessary for Distributed Private Learning? - Adam D. Smith 001; Abhradeep Thakurta; Jalaj Upadhyay (IEEE Symposium on Security and Privacy, 2017)

Information, Privacy and Stability in Adaptive Data Analysis. - Adam D. Smith 001 (CoRR, 2017)

Instantiability of RSA-OAEP Under Chosen-Plaintext Attack. - Eike Kiltz; Adam O'Neill; Adam D. Smith 001 (J. Cryptol., 2017)

Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation. - Christian Borgs; Jennifer T. Chayes; Adam D. Smith 001; Ilias Zadik (FOCS, 2018)

The Limits of Post-Selection Generalization. - Jonathan R. Ullman; Adam D. Smith 001; Kobbi Nissim; Uri Stemmer; Thomas Steinke 002 (NeurIPS, 2018)

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. - Blake E. Woodworth; Jialei Wang; Adam D. Smith 001; Brendan McMahan; Nati Srebro (NeurIPS, 2018)

Distributed Differential Privacy via Mixnets. - Albert Cheu; Adam D. Smith 001; Jonathan R. Ullman; David Zeber; Maxim Zhilyaev (CoRR, 2018)

Private Algorithms Can Always Be Extended. - Christian Borgs; Jennifer T. Chayes; Adam D. Smith 001; Ilias Zadik (CoRR, 2018)

Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation. - Di Wang 015; Adam D. Smith 001; Jinhui Xu 001 (CoRR, 2018)

Turning HATE Into LOVE: Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. - Leonid Reyzin; Adam D. Smith 001; Sophia Yakoubov (IACR Cryptol. ePrint Arch., 2018)

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. - Di Wang 015; Adam D. Smith 001; Jinhui Xu 001 (ALT, 2019)

From Soft Classifiers to Hard Decisions: How fair can we be? - Ran Canetti; Aloni Cohen; Nishanth Dikkala; Govind Ramnarayan; Sarah Scheffler; Adam D. Smith 001 (FAT, 2019)

The structure of optimal private tests for simple hypotheses. - Clément L. Canonne; Gautam Kamath 001; Audra McMillan; Adam D. Smith 001; Jonathan R. Ullman (STOC, 2019)

Distributed Differential Privacy via Shuffling. - Albert Cheu; Adam D. Smith 001; Jonathan R. Ullman; David Zeber; Maxim Zhilyaev (IACR Cryptol. ePrint Arch., 2019)

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. - Ryan Rogers 002; Aaron Roth 001; Adam D. Smith 001; Nathan Srebro; Om Thakkar 001; Blake E. Woodworth (AISTATS, 2020)

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. - Adam D. Smith 001; Shuang Song 001; Abhradeep Thakurta (NeurIPS, 2020)

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. - Di Wang 015; Marco Gaboardi; Adam D. Smith 001; Jinhui Xu 001 (J. Mach. Learn. Res., 2020)

When Are Fuzzy Extractors Possible? - Benjamin Fuller 001; Leonid Reyzin; Adam D. Smith 001 (IEEE Trans. Inf. Theory, 2020)

Turning HATE into LOVE: Compact Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. - Leonid Reyzin; Adam D. Smith 001; Sophia Yakoubov (CSCML, 2021)

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. - Gavin Brown 003; Marco Gaboardi; Adam D. Smith 001; Jonathan R. Ullman; Lydia Zakynthinou (NeurIPS, 2021)

Differentially Private Model Personalization. - Prateek Jain 002; John Rush; Adam D. Smith 001; Shuang Song 001; Abhradeep Guha Thakurta (NeurIPS, 2021)

When is memorization of irrelevant training data necessary for high-accuracy learning? - Gavin Brown 003; Mark Bun; Vitaly Feldman; Adam D. Smith 001; Kunal Talwar (STOC, 2021)

Non-parametric Differentially Private Confidence Intervals for the Median. - Jörg Drechsler; Ira Globus-Harris; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001 (CoRR, 2021)

Reusable Fuzzy Extractors for Low-Entropy Distributions. - Ran Canetti; Benjamin Fuller 001; Omer Paneth; Leonid Reyzin; Adam D. Smith 001 (J. Cryptol., 2021)

Manipulation Attacks in Local Differential Privacy. - Albert Cheu; Adam D. Smith 001; Jonathan R. Ullman (J. Priv. Confidentiality, 2021)

Algorithmic Stability for Adaptive Data Analysis. - Raef Bassily; Kobbi Nissim; Adam D. Smith 001; Thomas Steinke 002; Uri Stemmer; Jonathan R. Ullman (SIAM J. Comput., 2021)

Strong Memory Lower Bounds for Learning Natural Models. - Gavin Brown 003; Mark Bun; Adam D. Smith 001 (COLT, 2022)

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. - Sergey Denisov; H. Brendan McMahan; John Rush; Adam D. Smith 001; Abhradeep Guha Thakurta (NeurIPS, 2022)

Instance-Optimal Differentially Private Estimation. - Audra McMillan; Adam D. Smith 001; Jonathan R. Ullman (CoRR, 2022)

Fully Adaptive Composition for Gaussian Differential Privacy. - Adam D. Smith 001; Abhradeep Thakurta (CoRR, 2022)

Differentially Private Simple Linear Regression. - Daniel Alabi; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001; Salil P. Vadhan (Proc. Priv. Enhancing Technol., 2022)

Control, Confidentiality, and the Right to be Forgotten. - Aloni Cohen; Adam D. Smith 001; Marika Swanberg; Prashant Nalini Vasudevan (CCS, 2023)

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions. - Gavin Brown 003; Samuel B. Hopkins 001; Adam Smith 006 (COLT, 2023)

The Price of Differential Privacy under Continual Observation. - Palak Jain 004; Sofya Raskhodnikova; Satchit Sivakumar; Adam D. Smith 001 (ICML, 2023)

Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation. - Palak Jain 004; Iden Kalemaj; Sofya Raskhodnikova; Satchit Sivakumar; Adam Smith 006 (NeurIPS, 2023)

Hypothesis Selection with Memory Constraints. - Maryam Aliakbarpour; Mark Bun; Adam Smith 006 (NeurIPS, 2023)

Node-Differentially Private Estimation of the Number of Connected Components. - Iden Kalemaj; Sofya Raskhodnikova; Adam D. Smith 001; Charalampos E. Tsourakakis (PODS, 2023)

Differentially Private Confidence Intervals for Proportions under Stratified Random Sampling. - Shurong Lin; Mark Bun; Marco Gaboardi; Eric D. Kolaczyk; Adam Smith 006 (CoRR, 2023)

Metalearning with Very Few Samples Per Task. - Maryam Aliakbarpour; Konstantina Bairaktari; Gavin Brown 003; Adam Smith 006; Jonathan R. Ullman (CoRR, 2023)

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract. - Gavin Brown 003; Jonathan Hayase; Samuel B. Hopkins 001; Weihao Kong; Xiyang Liu; Sewoong Oh; Juan C. Perdomo; Adam Smith 006 (COLT, 2024)

Metalearning with Very Few Samples Per Task. - Maryam Aliakbarpour; Konstantina Bairaktari; Gavin Brown 003; Adam Smith 006; Nathan Srebro; Jonathan R. Ullman (COLT, 2024)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation. - Gavin Brown 003; Krishnamurthy Dj Dvijotham; Georgina Evans; Daogao Liu; Adam Smith 006; Abhradeep Guha Thakurta (ICML, 2024)

Optimal Hypothesis Selection in (Almost) Linear Time. - Maryam Aliakbarpour; Mark Bun; Adam Smith 006 (NeurIPS, 2024)

Auditing Privacy Mechanisms via Label Inference Attacks. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Andrés Muñoz Medina; Adam Smith 006; Marika Swanberg (NeurIPS, 2024)

Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation. - Palak Jain 004; Adam Smith 006; Connor Wagaman (SP, 2024)

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares. - Gavin Brown 003; Jonathan Hayase; Samuel B. Hopkins 001; Weihao Kong; Xiyang Liu; Sewoong Oh; Juan C. Perdomo; Adam Smith 006 (CoRR, 2024)

Privacy in Metalearning and Multitask Learning: Modeling and Separations. - Maryam Aliakbarpour; Konstantina Bairaktari; Adam Smith 006; Marika Swanberg; Jonathan R. Ullman (AISTATS, 2025)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. - Milad Nasr; Thomas Steinke 002; Borja Balle; Christopher A. Choquette-Choo; Arun Ganesh; Matthew Jagielski; Jamie Hayes; Abhradeep Guha Thakurta; Adam Smith 006; Andreas Terzis (ICLR, 2025)

Privately Evaluating Untrusted Black-Box Functions. - Ephraim Linder; Sofya Raskhodnikova; Adam Smith 006; Thomas Steinke 002 (STOC, 2025)

Triangle Counting With Local Edge Differential Privacy. - Talya Eden; Quanquan C. Liu; Sofya Raskhodnikova; Adam D. Smith 001 (Random Struct. Algorithms, 2025)

Differentially Private Sampling from Distributions. - Sofya Raskhodnikova; Satchit Sivakumar; Adam Smith 006; Marika Swanberg (SIAM J. Comput., 2025)

It's My Data Too: Private ML for Datasets with Multi-User Training Examples. - Arun Ganesh; Ryan McKenna; Brendan McMahan; Adam Smith 006; Fan Wu (CoRR, 2025)

Generate-then-Verify: Reconstructing Data from Limited Published Statistics. - Terrance Liu; Eileen Xiao; Pratiksha Thaker; Adam Smith 006; Zhiwei Steven Wu (CoRR, 2025)

Black-Box Privacy Attacks on Shared Representations in Multitask Learning. - John Abascal; Nicolás Berrios; Alina Oprea; Jonathan R. Ullman; Adam Smith 006; Matthew Jagielski (CoRR, 2025)

Beyond the Worst Case: Extending Differential Privacy Guarantees to Realistic Adversaries. - Marika Swanberg; Meenatchi Sundaram Muthu Selva Annamalai; Jamie Hayes; Borja Balle; Adam D. Smith 001 (CoRR, 2025)