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ORCIDAdam D. Smith
Person information
- affiliation: Boston University, Department of Computer Science, Boston, MA, USA
- affiliation (2007-2017): Pennsylvania State University, Computer Science and Engineering Department
- affiliation (former): Weizmann Institute of Science, Rehovot, Department of Computer Science and Applied Mathematics
- affiliation (former): McGill University, Montréal, School of Computer Scienc
- affiliation (1999-2004): MIT, Computer Science and AI Lab, Cambridge, MA, USA
- award (2008): Presidential Early Career Award for Scientists and Engineers
- award: Gödel Prize
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2020 – today
- 2023
[i64]Gavin Brown, Samuel B. Hopkins, Adam D. Smith:
Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions. CoRR abs/2301.12250 (2023)- [i63]Iden Kalemaj, Sofya Raskhodnikova, Adam D. Smith, Charalampos E. Tsourakakis:
Node-Differentially Private Estimation of the Number of Connected Components. CoRR abs/2304.05890 (2023) - [i62]Talya Eden, Quanquan C. Liu, Sofya Raskhodnikova, Adam D. Smith:
Triangle Counting with Local Edge Differential Privacy. CoRR abs/2305.02263 (2023) - 2022
- [j23]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. 2022(2): 184-204 (2022) - [c78]Gavin Brown, Mark Bun, Adam D. Smith:
Strong Memory Lower Bounds for Learning Natural Models. COLT 2022: 4989-5029 - [c77]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 - [i61]Gavin Brown, Mark Bun, Adam D. Smith:
Strong Memory Lower Bounds for Learning Natural Models. CoRR abs/2206.04743 (2022) - [i60]Aloni Cohen, Adam D. Smith, Marika Swanberg, Prashant Nalini Vasudevan:
Control, Confidentiality, and the Right to be Forgotten. CoRR abs/2210.07876 (2022) - [i59]Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
Instance-Optimal Differentially Private Estimation. CoRR abs/2210.15819 (2022) - [i58]Adam D. Smith, Abhradeep Thakurta:
Fully Adaptive Composition for Gaussian Differential Privacy. CoRR abs/2210.17520 (2022) - [i57]Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg:
Differentially Private Sampling from Distributions. CoRR abs/2211.08193 (2022) - 2021
- [j22]Ran Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam D. Smith:
Reusable Fuzzy Extractors for Low-Entropy Distributions. J. Cryptol. 34(1): 2 (2021) - [j21]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - [j20]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) - [c76]Leonid Reyzin, Adam D. Smith, Sophia Yakoubov:
Turning HATE into LOVE: Compact Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. CSCML 2021: 361-378 - [c75]Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou:
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. NeurIPS 2021: 7950-7964 - [c74]Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith, Marika Swanberg:
Differentially Private Sampling from Distributions. NeurIPS 2021: 28983-28994 - [c73]Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta:
Differentially Private Model Personalization. NeurIPS 2021: 29723-29735 - [c72]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. IEEE Symposium on Security and Privacy 2021: 883-900 - [c71]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: 123-132 - [i56]Jörg Drechsler, Ira Globus-Harris, Audra McMillan, Jayshree Sarathy, Adam D. Smith:
Non-parametric Differentially Private Confidence Intervals for the Median. CoRR abs/2106.10333 (2021) - [i55]Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou:
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. CoRR abs/2106.13329 (2021) - [i54]Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith:
The Price of Differential Privacy under Continual Observation. CoRR abs/2112.00828 (2021) - 2020
- [j19]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: 200:1-200:39 (2020) - [j18]Benjamin Fuller
, Leonid Reyzin, Adam D. Smith:
When Are Fuzzy Extractors Possible? IEEE Trans. Inf. Theory 66(8): 5282-5298 (2020) - [c70]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: 2830-2840 - [c69]Adam D. Smith, Shuang Song, Abhradeep Thakurta:
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. NeurIPS 2020 - [e4]Yael Tauman Kalai, Adam D. Smith, Daniel Wichs:
1st Conference on Information-Theoretic Cryptography, ITC 2020, June 17-19, 2020, Boston, MA, USA. LIPIcs 163, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2020, ISBN 978-3-95977-151-1 [contents] - [i53]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. CoRR abs/2007.05157 (2020) - [i52]Di Wang, Marco Gaboardi, Adam D. Smith, Jinhui Xu:
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy. CoRR abs/2011.05934 (2020) - [i51]Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar:
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? CoRR abs/2012.06421 (2020)
2010 – 2019
- 2019
- [c68]Di Wang, Adam D. Smith, Jinhui Xu:
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. ALT 2019: 897-902 - [c67]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. EUROCRYPT (1) 2019: 375-403 - [c66]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: 309-318 - [c65]Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
The structure of optimal private tests for simple hypotheses. STOC 2019: 310-321 - [i50]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - [i49]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. CoRR abs/1909.09630 (2019) - [i48]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. IACR Cryptol. ePrint Arch. 2019: 245 (2019) - 2018
- [c64]Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik:
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation. FOCS 2018: 533-543 - [c63]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [c62]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: 8505-8515 - [i47]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - [i46]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Mixnets. CoRR abs/1808.01394 (2018) - [i45]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 abs/1810.02003 (2018) - [i44]Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik:
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation. CoRR abs/1810.02183 (2018) - [i43]Christian Borgs, Jennifer T. Chayes, Adam D. Smith, Ilias Zadik:
Private Algorithms Can Always Be Extended. CoRR abs/1810.12518 (2018) - [i42]Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR abs/1811.11148 (2018) - [i41]Di Wang, Adam D. Smith, Jinhui Xu:
Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation. CoRR abs/1812.06825 (2018) - [i40]Leonid Reyzin, Adam D. Smith, Sophia Yakoubov:
Turning HATE Into LOVE: Homomorphic Ad Hoc Threshold Encryption for Scalable MPC. IACR Cryptol. ePrint Arch. 2018: 997 (2018) - 2017
- [j17]Eike Kiltz, Adam O'Neill, Adam D. Smith:
Instantiability of RSA-OAEP Under Chosen-Plaintext Attack. J. Cryptol. 30(3): 889-919 (2017) - [c61]Adam D. Smith, Abhradeep Thakurta, Jalaj Upadhyay:
Is Interaction Necessary for Distributed Private Learning? IEEE Symposium on Security and Privacy 2017: 58-77 - [i39]Adam D. Smith:
Information, Privacy and Stability in Adaptive Data Analysis. CoRR abs/1706.00820 (2017) - 2016
- [j16]Venkatesan Guruswami, Adam D. Smith:
Optimal Rate Code Constructions for Computationally Simple Channels. J. ACM 63(4): 35:1-35:37 (2016) - [j15]Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam D. Smith:
Calibrating Noise to Sensitivity in Private Data Analysis. J. Priv. Confidentiality 7(3): 17-51 (2016) - [c60]Benjamin Fuller, Leonid Reyzin, Adam D. Smith:
When Are Fuzzy Extractors Possible? ASIACRYPT (1) 2016: 277-306 - [c59]Ran Canetti, Benjamin Fuller, Omer Paneth, Leonid Reyzin, Adam D. Smith:
Reusable Fuzzy Extractors for Low-Entropy Distributions. EUROCRYPT (1) 2016: 117-146 - [c58]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS 2016: 487-494 - [c57]Sofya Raskhodnikova, Adam D. Smith:
Lipschitz Extensions for Node-Private Graph Statistics and the Generalized Exponential Mechanism. FOCS 2016: 495-504 - [c56]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [e3]Martin Hirt, Adam D. Smith:
Theory of Cryptography - 14th International Conference, TCC 2016-B, Beijing, China, October 31 - November 3, 2016, Proceedings, Part I. Lecture Notes in Computer Science 9985, 2016, ISBN 978-3-662-53640-7 [contents] - [e2]Martin Hirt, Adam D. Smith:
Theory of Cryptography - 14th International Conference, TCC 2016-B, Beijing, China, October 31 - November 3, 2016, Proceedings, Part II. Lecture Notes in Computer Science 9986, 2016, ISBN 978-3-662-53643-8 [contents] - [r1]Sofya Raskhodnikova, Adam D. Smith:
Differentially Private Analysis of Graphs. Encyclopedia of Algorithms 2016: 543-547 - [i38]Audra McMillan, Adam D. Smith:
When is Nontrivial Estimation Possible for Graphons and Stochastic Block Models? CoRR abs/1604.01871 (2016) - [i37]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR abs/1604.03924 (2016) - 2015
- [j14]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): 820-843 (2015) - [c55]Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan:
Robust Traceability from Trace Amounts. FOCS 2015: 650-669 - [c54]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. ITCS 2015: 173-180 - [c53]Christian Borgs, Jennifer T. Chayes, Adam D. Smith:
Private Graphon Estimation for Sparse Graphs. NIPS 2015: 1369-1377 - [c52]Raef Bassily, Adam D. Smith:
Local, Private, Efficient Protocols for Succinct Histograms. STOC 2015: 127-135 - [c51]Adam D. Smith, Ye Zhang:
On the Regularity of Lossy RSA - Improved Bounds and Applications to Padding-Based Encryption. TCC (1) 2015: 609-628 - [i36]Raef Bassily, Adam D. Smith:
Local, Private, Efficient Protocols for Succinct Histograms. CoRR abs/1504.04686 (2015) - [i35]Sofya Raskhodnikova, Adam D. Smith:
Efficient Lipschitz Extensions for High-Dimensional Graph Statistics and Node Private Degree Distributions. CoRR abs/1504.07912 (2015) - [i34]Christian Borgs, Jennifer T. Chayes, Adam D. Smith:
Private Graphon Estimation for Sparse Graphs. CoRR abs/1506.06162 (2015) - [i33]Sean Hallgren, Adam D. Smith, Fang Song:
Classical Cryptographic Protocols in a Quantum World. CoRR abs/1507.01625 (2015) - [i32]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic Stability for Adaptive Data Analysis. CoRR abs/1511.02513 (2015) - [i31]Adam D. Smith, Ye Zhang:
On the Regularity of Lossy RSA: Improved Bounds and Applications to Padding-Based Encryption. IACR Cryptol. ePrint Arch. 2015: 27 (2015) - [i30]Sean Hallgren, Adam D. Smith, Fang Song:
Classical Cryptographic Protocols in a Quantum World. IACR Cryptol. ePrint Arch. 2015: 687 (2015) - 2014
- [j13]Shiva Prasad Kasiviswanathan, Adam D. Smith:
On the 'Semantics' of Differential Privacy: A Bayesian Formulation. J. Priv. Confidentiality 6(1) (2014) - [j12]Vishesh Karwa, Sofya Raskhodnikova, Adam D. Smith, Grigory Yaroslavtsev:
Private Analysis of Graph Structure. ACM Trans. Database Syst. 39(3): 22:1-22:33 (2014) - [c50]Raef Bassily, Adam D. Smith, Abhradeep Thakurta:
Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. FOCS 2014: 464-473 - [c49]Raef Bassily, Adam D. Smith:
Causal Erasure Channels. SODA 2014: 1844-1857 - [i29]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. CoRR abs/1402.4488 (2014) - [i28]Raef Bassily, Adam D. Smith, Abhradeep Thakurta:
Private Empirical Risk Minimization, Revisited. CoRR abs/1405.7085 (2014) - [i27]Raef Bassily, Adam D. Smith:
Causal Erasure Channels. CoRR abs/1409.3893 (2014) - [i26]Benjamin Fuller, Adam D. Smith, Leonid Reyzin:
Where are Fuzzy Extractors Possible? IACR Cryptol. ePrint Arch. 2014: 961 (2014) - 2013
- [j11]Sofya Raskhodnikova, Dana Ron, Ronitt Rubinfeld, Adam D. Smith:
Sublinear Algorithms for Approximating String Compressibility. Algorithmica 65(3): 685-709 (2013) - [c48]Abhradeep Thakurta, Adam D. Smith:
Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso. COLT 2013: 819-850 - [c47]Mark Lewko, Adam O'Neill, Adam D. Smith:
Regularity of Lossy RSA on Subdomains and Its Applications. EUROCRYPT 2013: 55-75 - [c46]Raef Bassily, Adam Groce, Jonathan Katz, Adam D. Smith:
Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy. FOCS 2013: 439-448 - [c45]Abhradeep Guha Thakurta, Adam D. Smith:
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings. NIPS 2013: 2733-2741 - [c44]Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith:
The Power of Linear Reconstruction Attacks. SODA 2013: 1415-1433 - [c43]Shiva Prasad Kasiviswanathan, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
Analyzing Graphs with Node Differential Privacy. TCC 2013: 457-476 - [i25]Adam D. Smith, Ye Zhang:
Near-linear time, Leakage-resilient Key Evolution Schemes from Expander Graphs. IACR Cryptol. ePrint Arch. 2013: 864 (2013) - 2012
- [j10]Aleksandra B. Slavkovic, Adam D. Smith:
Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy. J. Priv. Confidentiality 4(1) (2012) - [j9]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): 6207-6222 (2012) - [c42]Daniel Kifer, Adam D. Smith, Abhradeep Thakurta:
Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression. COLT 2012: 25.1-25.40 - [e1]Adam D. Smith:
Information Theoretic Security - 6th International Conference, ICITS 2012, Montreal, QC, Canada, August 15-17, 2012. Proceedings. Lecture Notes in Computer Science 7412, Springer 2012, ISBN 978-3-642-32283-9 [contents] - [i24]Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith:
The Power of Linear Reconstruction Attacks. CoRR abs/1210.2381 (2012) - 2011
- [j8]Vishesh Karwa, Sofya Raskhodnikova, Adam D. Smith, Grigory Yaroslavtsev:
Private Analysis of Graph Structure. Proc. VLDB Endow. 4(11): 1146-1157 (2011) - [j7]Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
What Can We Learn Privately? SIAM J. Comput. 40(3): 793-826 (2011) - [j6]Marco Tomamichel, Christian Schaffner, Adam D. Smith, Renato Renner:
Leftover Hashing Against Quantum Side Information. IEEE Trans. Inf. Theory 57(8): 5524-5535 (2011) - [c41]Sean Hallgren, Adam D. Smith, Fang Song:
Classical Cryptographic Protocols in a Quantum World. CRYPTO 2011: 411-428 - [c40]Adam D. Smith:
Privacy-preserving statistical estimation with optimal convergence rates. STOC 2011: 813-822 - [i23]Eike Kiltz, Adam O'Neill, Adam D. Smith:
Instantiability of RSA-OAEP under Chosen-Plaintext Attack. IACR Cryptol. ePrint Arch. 2011: 559 (2011) - 2010
- [j5]Jonathan Katz, Ji Sun Shin, Adam D. Smith:
Parallel and Concurrent Security of the HB and HB+ Protocols. J. Cryptol. 23(3): 402-421 (2010) - [j4]Cynthia Dwork, Adam D. Smith:
Differential Privacy for Statistics: What we Know and What we Want to Learn. J. Priv. Confidentiality 1(2) (2010) - [c39]Eike Kiltz, Adam O'Neill, Adam D. Smith:
Instantiability of RSA-OAEP under Chosen-Plaintext Attack. CRYPTO 2010: 295-313 - [c38]Venkatesan Guruswami, Adam D. Smith:
Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate. FOCS 2010: 723-732 - [c37]Marco Tomamichel, Renato Renner, Christian Schaffner, Adam D. Smith:
Leftover Hashing against quantum side information. ISIT 2010: 2703-2707 - [c36]Raghav Bhaskar, Srivatsan Laxman, Adam D. Smith, Abhradeep Thakurta:
Discovering frequent patterns in sensitive data. KDD 2010: 503-512 - [c35]Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith, Jonathan R. Ullman:
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. STOC 2010: 775-784 - [i22]Venkatesan Guruswami, Adam D. Smith:
Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate. CoRR abs/1004.4017 (2010) - [i21]Venkatesan Guruswami, Adam D. Smith:
Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate. Electron. Colloquium Comput. Complex. TR10 (2010) - [i20]Yevgeniy Dodis, Bhavana Kanukurthi, Jonathan Katz, Leonid Reyzin, Adam D. Smith:
Robust Fuzzy Extractors and Authenticated Key Agreement from Close Secrets. IACR Cryptol. ePrint Arch. 2010: 456 (2010)
2000 – 2009
- 2009
- [j3]Sofya Raskhodnikova, Dana Ron, Amir Shpilka, Adam D. Smith:
Strong Lower Bounds for Approximating Distribution Support Size and the Distinct Elements Problem. SIAM J. Comput. 39(3): 813-842 (2009) - [c34]Adam D. Smith:
Asymptotically Optimal and Private Statistical Estimation. CANS 2009: 53-57 - [c33]Adam D. Smith:
What Can Cryptography Do for Coding Theory? ICITS 2009: 158 - [c32]Yevgeniy Dodis, Jonathan Katz, Adam D. Smith, Shabsi Walfish:
Composability and On-Line Deniability of Authentication. TCC 2009: 146-162 - 2008
- [j2]Yevgeniy Dodis, Rafail Ostrovsky, Leonid Reyzin, Adam D. Smith:
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. SIAM J. Comput. 38(1): 97-139 (2008) - [j1]Moni Naor, Gil Segev, Adam D. Smith:
Tight Bounds for Unconditional Authentication Protocols in the Manual Channel and Shared Key Models. IEEE Trans. Inf. Theory 54(6): 2408-2425 (2008) - [c31]William Enck, Kevin R. B. Butler, Thomas Richardson, Patrick D. McDaniel, Adam D. Smith:
Defending Against Attacks on Main Memory Persistence. ACSAC 2008: 65-74 - [c30]Ivan Damgård, Yuval Ishai, Mikkel Krøigaard, Jesper Buus Nielsen, Adam D. Smith:
Scalable Multiparty Computation with Nearly Optimal Work and Resilience. CRYPTO 2008: 241-261 - [c29]Vipul Goyal, Payman Mohassel, Adam D. Smith:
Efficient Two Party and Multi Party Computation Against Covert Adversaries. EUROCRYPT 2008: 289-306 - [c28]Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam D. Smith:
What Can We Learn Privately? FOCS 2008: 531-540 - [c27]Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, Adam D. Smith:
Composition attacks and auxiliary information in data privacy. KDD 2008: 265-273 - [i19]Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, Adam D. Smith:
Composition Attacks and Auxiliary Information in Data Privacy. CoRR abs/0803.0032 (2008) - [i18]