Adam D. Smith

According to our database, Adam D. Smith authored at least 119 papers between 1999 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD.

Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle, Christopher A. Choquette-Choo, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam D. Smith, Andreas Terzis

CoRR, 2024

Auditing Privacy Mechanisms via Label Inference Attacks.

Róbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam D. Smith, Marika Swanberg

CoRR, 2024

Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation.

Palak Jain, Adam D. Smith, Connor Wagaman

Proceedings of the IEEE Symposium on Security and Privacy, 2024

2023

Node-Differentially Private Estimation of the Number of Connected Components.

Iden Kalemaj, Sofya Raskhodnikova, Adam D. Smith, Charalampos E. Tsourakakis

Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2023

Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation.

Palak Jain, Iden Kalemaj, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Price of Differential Privacy under Continual Observation.

Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith

Proceedings of the International Conference on Machine Learning, 2023

Triangle Counting with Local Edge Differential Privacy.

Talya Eden, Quanquan C. Liu, Sofya Raskhodnikova, Adam D. Smith

Proceedings of the 50th International Colloquium on Automata, Languages, and Programming, 2023

Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions.

Gavin Brown, Samuel B. Hopkins, Adam D. Smith

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Control, Confidentiality, and the Right to be Forgotten.

Aloni Cohen, Adam D. Smith, Marika Swanberg, Prashant Nalini Vasudevan

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022

Differentially Private Simple Linear Regression.

Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan

Proc. Priv. Enhancing Technol., 2022

Fully Adaptive Composition for Gaussian Differential Privacy.

Adam D. Smith, Abhradeep Thakurta

CoRR, 2022

Instance-Optimal Differentially Private Estimation.

Audra McMillan, Adam D. Smith, Jonathan R. Ullman

CoRR, 2022

Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams.

Sergey Denisov, H. Brendan McMahan, John Rush, Adam D. Smith, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Strong Memory Lower Bounds for Learning Natural Models.

Gavin Brown, Mark Bun, Adam D. Smith

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Algorithmic Stability for Adaptive Data Analysis.

Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman

SIAM J. Comput., 2021

Manipulation Attacks in Local Differential Privacy.

Albert Cheu, Adam D. Smith, Jonathan R. Ullman

J. Priv. Confidentiality, 2021

Reusable Fuzzy Extractors for Low-Entropy Distributions.

Ran Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam D. Smith

J. Cryptol., 2021

Non-parametric Differentially Private Confidence Intervals for the Median.

Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith

CoRR, 2021

When is memorization of irrelevant training data necessary for high-accuracy learning?

Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Differentially Private Sampling from Distributions.

Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially Private Model Personalization.

Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Covariance-Aware Private Mean Estimation Without Private Covariance Estimation.

Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Turning HATE into LOVE: Compact Homomorphic Ad Hoc Threshold Encryption for Scalable MPC.

Leonid Reyzin, Adam D. Smith, Sophia Yakoubov

Proceedings of the Cyber Security Cryptography and Machine Learning, 2021

2020

When Are Fuzzy Extractors Possible?

Benjamin Fuller, Leonid Reyzin, Adam D. Smith

IEEE Trans. Inf. Theory, 2020

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy.

Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu

J. Mach. Learn. Res., 2020

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space.

Adam D. Smith, Shuang Song, Abhradeep Thakurta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.

Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Distributed Differential Privacy via Shuffling.

Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev

IACR Cryptol. ePrint Arch., 2019

The structure of optimal private tests for simple hypotheses.

Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 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

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations.

Di Wang, Adam D. Smith, Jinhui Xu

Proceedings of the Algorithmic Learning Theory, 2019

2018

Turning HATE Into LOVE: Homomorphic Ad Hoc Threshold Encryption for Scalable MPC.

Leonid Reyzin, Adam D. Smith, Sophia Yakoubov

IACR Cryptol. ePrint Arch., 2018

Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation.

Di Wang, Adam D. Smith, Jinhui Xu

CoRR, 2018

Private Algorithms Can Always Be Extended.

Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik

CoRR, 2018

Distributed Differential Privacy via Mixnets.

Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev

CoRR, 2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization.

Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Limits of Post-Selection Generalization.

Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation.

Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik

Proceedings of the 59th IEEE Annual Symposium on Foundations of Computer Science, 2018

2017

Instantiability of RSA-OAEP Under Chosen-Plaintext Attack.

Eike Kiltz, Adam O'Neill, Adam D. Smith

J. Cryptol., 2017

Information, Privacy and Stability in Adaptive Data Analysis.

Adam D. Smith

CoRR, 2017

Is Interaction Necessary for Distributed Private Learning?

Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay

Proceedings of the 2017 IEEE Symposium on Security and Privacy, 2017

2016

Differentially Private Analysis of Graphs.

Sofya Raskhodnikova, Adam D. Smith

Encyclopedia of Algorithms, 2016

Calibrating Noise to Sensitivity in Private Data Analysis.

Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith

J. Priv. Confidentiality, 2016

Optimal Rate Code Constructions for Computationally Simple Channels.

Venkatesan Guruswami, Adam D. Smith

J. ACM, 2016

When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models?

Audra McMillan, Adam D. Smith

CoRR, 2016

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing.

Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar

Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism.

Sofya Raskhodnikova, Adam D. Smith

Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

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

J. Cryptol., 2015

On the Regularity of Lossy RSA: Improved Bounds and Applications to Padding-Based Encryption.

Adam D. Smith, Ye Zhang

IACR Cryptol. ePrint Arch., 2015

Classical Cryptographic Protocols in a Quantum World.

Sean Hallgren, Adam D. Smith, Fang Song

IACR Cryptol. ePrint Arch., 2015

Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions.

Sofya Raskhodnikova, Adam D. Smith

CoRR, 2015

Local, Private, Efficient Protocols for Succinct Histograms.

Raef Bassily, Adam D. Smith

Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015

Private Graphon Estimation for Sparse Graphs.

Christian Borgs, Jennifer T. Chayes, Adam D. Smith

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Privacy-Preserving Public Information for Sequential Games.

Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015

Robust Traceability from Trace Amounts.

Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan

Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science, 2015

2014

On the 'Semantics' of Differential Privacy: A Bayesian Formulation.

Shiva Prasad Kasiviswanathan, Adam D. Smith

J. Priv. Confidentiality, 2014

Where are Fuzzy Extractors Possible?

Benjamin Fuller, Adam D. Smith, Leonid Reyzin

IACR Cryptol. ePrint Arch., 2014

Private Empirical Risk Minimization, Revisited.

Raef Bassily, Adam D. Smith, Abhradeep Thakurta

CoRR, 2014

Causal Erasure Channels.

Raef Bassily, Adam D. Smith

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2014

Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds.

Raef Bassily, Adam D. Smith, Abhradeep Thakurta

Proceedings of the 55th IEEE Annual Symposium on Foundations of Computer Science, 2014

2013

Near-linear time, Leakage-resilient Key Evolution Schemes from Expander Graphs.

Adam D. Smith, Ye Zhang

IACR Cryptol. ePrint Arch., 2013

Sublinear Algorithms for Approximating String Compressibility.

Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Adam D. Smith

Algorithmica, 2013

Analyzing Graphs with Node Differential Privacy.

Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith

Proceedings of the Theory of Cryptography - 10th Theory of Cryptography Conference, 2013

The Power of Linear Reconstruction Attacks.

Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith

Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, 2013

(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings.

Abhradeep Guha Thakurta, Adam D. Smith

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy.

Raef Bassily, Adam Groce, Jonathan Katz, Adam D. Smith

Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

Regularity of Lossy RSA on Subdomains and Its Applications.

Mark Lewko, Adam O'Neill, Adam D. Smith

Proceedings of the Advances in Cryptology, 2013

Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso.

Abhradeep Thakurta, Adam D. Smith

Proceedings of the COLT 2013, 2013

2012

Robust Fuzzy Extractors and Authenticated Key Agreement From Close Secrets.

Yevgeniy Dodis, Bhavana Kanukurthi, Jonathan Katz, Leonid Reyzin, Adam D. Smith

IEEE Trans. Inf. Theory, 2012

Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy.

Aleksandra B. Slavkovic, Adam D. Smith

J. Priv. Confidentiality, 2012

Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression.

Daniel Kifer, Adam D. Smith, Abhradeep Thakurta

Proceedings of the COLT 2012, 2012

2011

Leftover Hashing Against Quantum Side Information.

Marco Tomamichel, Christian Schaffner, Adam D. Smith, Renato Renner

IEEE Trans. Inf. Theory, 2011

What Can We Learn Privately?

Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith

SIAM J. Comput., 2011

Private Analysis of Graph Structure.

Vishesh Karwa, Sofya Raskhodnikova, Adam D. Smith, Grigory Yaroslavtsev

Proc. VLDB Endow., 2011

Privacy-preserving statistical estimation with optimal convergence rates.

Adam D. Smith

Proceedings of the 43rd ACM Symposium on Theory of Computing, 2011

2010

Differential Privacy for Statistics: What we Know and What we Want to Learn.

Cynthia Dwork, Adam D. Smith

J. Priv. Confidentiality, 2010

Parallel and Concurrent Security of the HB and HB⁺ Protocols.

Jonathan Katz, Ji Sun Shin, Adam D. Smith

J. Cryptol., 2010

Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate.

Venkatesan Guruswami, Adam D. Smith

Electron. Colloquium Comput. Complex., 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, Jonathan R. Ullman

Proceedings of the 42nd ACM Symposium on Theory of Computing, 2010

Discovering frequent patterns in sensitive data.

Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

2009

Strong Lower Bounds for Approximating Distribution Support Size and the Distinct Elements Problem.

Sofya Raskhodnikova, Dana Ron, Amir Shpilka, Adam D. Smith

SIAM J. Comput., 2009

Composability and On-Line Deniability of Authentication.

Yevgeniy Dodis, Jonathan Katz, Adam D. Smith, Shabsi Walfish

Proceedings of the Theory of Cryptography, 6th Theory of Cryptography Conference, 2009

What Can Cryptography Do for Coding Theory?

Adam D. Smith

Proceedings of the Information Theoretic Security, 4th International Conference, 2009

Asymptotically Optimal and Private Statistical Estimation.

Adam D. Smith

Proceedings of the Cryptology and Network Security, 8th International Conference, 2009

2008

Tight Bounds for Unconditional Authentication Protocols in the Manual Channel and Shared Key Models.

Moni Naor, Gil Segev, Adam D. Smith

IEEE Trans. Inf. Theory, 2008

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data.

Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin, Adam D. Smith

SIAM J. Comput., 2008

Efficient, Differentially Private Point Estimators

Adam D. Smith

CoRR, 2008

A Note on Differential Privacy: Defining Resistance to Arbitrary Side Information

Shiva Prasad Kasiviswanathan, Adam D. Smith

CoRR, 2008

Composition attacks and auxiliary information in data privacy.

Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, Adam D. Smith

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Efficient Two Party and Multi Party Computation Against Covert Adversaries.

Vipul Goyal, Payman Mohassel, Adam D. Smith

Proceedings of the Advances in Cryptology, 2008

Scalable Multiparty Computation with Nearly Optimal Work and Resilience.

Ivan Damgård, Yuval Ishai, Mikkel Krøigaard, Jesper Buus Nielsen, Adam D. Smith

Proceedings of the Advances in Cryptology, 2008

Defending Against Attacks on Main Memory Persistence.

William Enck, Kevin R. B. Butler, Thomas Richardson, Patrick D. McDaniel, Adam D. Smith

Proceedings of the Twenty-Fourth Annual Computer Security Applications Conference, 2008

2007

Smooth sensitivity and sampling in private data analysis.

Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith

Proceedings of the 39th Annual ACM Symposium on Theory of Computing, 2007

2006

Scrambling Adversarial Errors Using Few Random Bits, Optimal Information Reconciliation, and Better Private Codes.

Adam D. Smith

IACR Cryptol. ePrint Arch., 2006

Analyzing the HB and HB+ Protocols in the "Large Error" Case.

Jonathan Katz, Adam D. Smith

IACR Cryptol. ePrint Arch., 2006

A Note on Adaptivity in Testing Properties of Bounded Degree Graphs.

Sofya Raskhodnikova, Adam D. Smith

Electron. Colloquium Comput. Complex., 2006

Secure Multiparty Quantum Computation with (Only) a Strict Honest Majority.

Michael Ben-Or, Claude Crépeau, Daniel Gottesman, Avinatan Hassidim, Adam D. Smith

Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), 2006

Robust Fuzzy Extractors and Authenticated Key Agreement from Close Secrets.

Yevgeniy Dodis, Jonathan Katz, Leonid Reyzin, Adam D. Smith

Proceedings of the Advances in Cryptology, 2006

2005

Sublinear Algorithms for Approximating String Compressibility and the Distribution Support Size

Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Amir Shpilka, Adam D. Smith

Electron. Colloquium Comput. Complex., 2005

Toward Privacy in Public Databases.

Shuchi Chawla, Cynthia Dwork, Frank McSherry, Adam D. Smith, Hoeteck Wee

Proceedings of the Theory of Cryptography, Second Theory of Cryptography Conference, 2005

Correcting errors without leaking partial information.

Yevgeniy Dodis, Adam D. Smith

Proceedings of the 37th Annual ACM Symposium on Theory of Computing, 2005

Approximate Quantum Error-Correcting Codes and Secret Sharing Schemes.

Claude Crépeau, Daniel Gottesman, Adam D. Smith

Proceedings of the Advances in Cryptology, 2005

Secure Remote Authentication Using Biometric Data.

Xavier Boyen, Yevgeniy Dodis, Jonathan Katz, Rafail Ostrovsky, Adam D. Smith

Proceedings of the Advances in Cryptology, 2005

2004

Maintaining secrecy when information leakage is unavoidable.

Adam D. Smith

PhD thesis, 2004

Efficient Consistency Proofs for Generalized Queries on a Committed Database.

Rafail Ostrovsky, Charles Rackoff, Adam D. Smith

IACR Cryptol. ePrint Arch., 2004

Entropic Security and the Encryption of High Entropy Messages.

Yevgeniy Dodis, Adam D. Smith

IACR Cryptol. ePrint Arch., 2004

List-Decoding of Linear Functions and Analysis of a Two-Round Zero-Knowledge Argument.

Cynthia Dwork, Ronen Shaltiel, Adam D. Smith, Luca Trevisan

Proceedings of the Theory of Cryptography, First Theory of Cryptography Conference, 2004

Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data.

Yevgeniy Dodis, Leonid Reyzin, Adam D. Smith

Proceedings of the Advances in Cryptology, 2004

Small Pseudo-random Families of Matrices: Derandomizing Approximate Quantum Encryption.

Andris Ambainis, Adam D. Smith

Proceedings of the Approximation, 2004

2003

Lower bounds for collusion-secure fingerprinting.

Chris Peikert, Abhi Shelat, Adam D. Smith

Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2003

Round Efficiency of Multi-party Computation with a Dishonest Majority.

Jonathan Katz, Rafail Ostrovsky, Adam D. Smith

Proceedings of the Advances in Cryptology, 2003

2002

Authentication of Quantum Messages.

Howard Barnum, Claude Crépeau, Daniel Gottesman, Adam D. Smith, Alain Tapp

IACR Cryptol. ePrint Arch., 2002

Secure multi-party quantum computation.

Claude Crépeau, Daniel Gottesman, Adam D. Smith

Proceedings of the Proceedings on 34th Annual ACM Symposium on Theory of Computing, 2002

Detectable byzantine agreement secure against faulty majorities.

Matthias Fitzi, Daniel Gottesman, Martin Hirt, Thomas Holenstein, Adam D. Smith

Proceedings of the Twenty-First Annual ACM Symposium on Principles of Distributed Computing, 2002

Extracting Quantum Entanglement.

Andris Ambainis, Adam D. Smith, Ke Yang

Proceedings of the 17th Annual IEEE Conference on Computational Complexity, 2002

2001

Efficient and Non-Interactive Non-Malleable Commitment.

Giovanni Di Crescenzo, Jonathan Katz, Rafail Ostrovsky, Adam D. Smith

IACR Cryptol. ePrint Arch., 2001

On Perfect and Adaptive Security in Exposure-Resilient Cryptography.

Yevgeniy Dodis, Amit Sahai, Adam D. Smith

Proceedings of the Advances in Cryptology, 2001

Mutually Independent Commitments.

Moses D. Liskov, Anna Lysyanskaya, Silvio Micali, Leonid Reyzin, Adam D. Smith

Proceedings of the Advances in Cryptology, 2001

1999

Multiparty computation unconditionally secure against Q^2 adversary structures

Adam D. Smith, Anton Stiglic

CoRR, 1999