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2020 – today
- 2024
[j24]Jiawen Liu
, Weihao Qu, Marco Gaboardi, Deepak Garg, Jonathan R. Ullman:
Program Analysis for Adaptive Data Analysis. Proc. ACM Program. Lang. 8(PLDI): 914-938 (2024)- [j23]John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. Proc. Priv. Enhancing Technol. 2024(3): 202-223 (2024) - [j22]Liudas Panavas, Tarik Crnovrsanin, Jane Lydia Adams, Jonathan R. Ullman, Ali Sarvghad, Melanie Tory, Cody Dunne:
Investigating the Visual Utility of Differentially Private Scatterplots. IEEE Trans. Vis. Comput. Graph. 30(8): 5370-5385 (2024) - [c64]Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. COLT 2024: 46-93 - [c63]Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. COLT 2024: 4207-4239 - [c62]Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. ICLR 2024 - [c61]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML 2024 - [c60]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS 2024: 3:1-3:21 - [i70]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR abs/2402.11173 (2024) - [i69]Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman:
Private Mean Estimation with Person-Level Differential Privacy. CoRR abs/2405.20405 (2024) - [i68]Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman:
Private Geometric Median. CoRR abs/2406.07407 (2024) - 2023
- [j21]Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. 2023(3): 211-232 (2023) - [j20]Konstantina Bairaktari, Paul Langton, Huy L. Nguyen, Niklas Smedemark-Margulies, Jonathan R. Ullman:
Fair and Useful Cohort Selection. Trans. Mach. Learn. Res. 2023 (2023) - [c59]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. COLT 2023: 747-772 - [c58]Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. ICML 2023: 1121-1146 - [c57]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. SP 2023: 400-417 - [i67]Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR abs/2301.13334 (2023) - [i66]Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. CoRR abs/2302.01855 (2023) - [i65]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR abs/2305.13440 (2023) - [i64]John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. CoRR abs/2306.01181 (2023) - [i63]Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. CoRR abs/2307.07604 (2023) - [i62]Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. CoRR abs/2310.03838 (2023) - [i61]Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. CoRR abs/2312.13978 (2023) - 2022
- [c56]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT 2022: 544-572 - [i60]Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR abs/2205.06369 (2022) - [i59]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR abs/2208.12348 (2022) - [i58]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. CoRR abs/2209.03112 (2022) - [i57]Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
Instance-Optimal Differentially Private Estimation. CoRR abs/2210.15819 (2022) - 2021
- [j19]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - [j18]Adam Sealfon, Jonathan R. Ullman:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - [j17]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) - [c55]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. ICML 2021: 6968-6977 - [c54]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 - [c53]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. SP 2021: 883-900 - [c52]Albert Cheu, Jonathan R. Ullman:
The limits of pan privacy and shuffle privacy for learning and estimation. STOC 2021: 1081-1094 - [i56]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. CoRR abs/2102.08598 (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]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR abs/2111.04609 (2021) - 2020
- [j16]Jonathan R. Ullman, Salil P. Vadhan:
PCPs and the Hardness of Generating Synthetic Data. J. Cryptol. 33(4): 2078-2112 (2020) - [j15]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10(1) (2020) - [j14]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8(1): 6:1-6:35 (2020) - [c51]Huy Le Nguyen, Jonathan R. Ullman, Lydia Zakynthinou:
Efficient Private Algorithms for Learning Large-Margin Halfspaces. ALT 2020: 704-724 - [c50]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. COLT 2020: 2204-2235 - [c49]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. ICML 2020: 695-703 - [c48]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA 2020: 1-62 - [c47]Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman:
CoinPress: Practical Private Mean and Covariance Estimation. NeurIPS 2020 - [c46]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. NeurIPS 2020 - [c45]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS 2020 - [c44]Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman:
The power of factorization mechanisms in local and central differential privacy. STOC 2020: 425-438 - [i53]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. CoRR abs/2002.09464 (2020) - [i52]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. CoRR abs/2004.10941 (2020) - [i51]Gautam Kamath, Jonathan R. Ullman:
A Primer on Private Statistics. CoRR abs/2005.00010 (2020) - [i50]Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman:
CoinPress: Practical Private Mean and Covariance Estimation. CoRR abs/2006.06618 (2020) - [i49]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR abs/2006.07709 (2020) - [i48]Albert Cheu, Jonathan R. Ullman:
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation. CoRR abs/2009.08000 (2020)
2010 – 2019
- 2019
- [j13]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [j12]Jonathan R. Ullman, Lars Vilhuber:
Editorial for Volume 9 Issue 2. J. Priv. Confidentiality 9(2) (2019) - [j11]Jonathan R. Ullman, Lars Vilhuber:
Program for TPDP 2017. J. Priv. Confidentiality 9(2) (2019) - [c43]Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman:
Securely Sampling Biased Coins with Applications to Differential Privacy. CCS 2019: 603-614 - [c42]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. COLT 2019: 1853-1902 - [c41]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. EUROCRYPT (1) 2019: 375-403 - [c40]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c39]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS 2019: 168-180 - [c38]Jonathan R. Ullman, Adam Sealfon:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. NeurIPS 2019: 3765-3775 - [c37]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 - [i47]Huy L. Nguyen, Jonathan R. Ullman, Lydia Zakynthinou:
Efficient Private Algorithms for Learning Halfspaces. CoRR abs/1902.09009 (2019) - [i46]Adam Sealfon, Jonathan R. Ullman:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. CoRR abs/1905.10477 (2019) - [i45]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. CoRR abs/1905.11947 (2019) - [i44]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR abs/1909.03951 (2019) - [i43]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. CoRR abs/1909.09630 (2019) - [i42]Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman:
The Power of Factorization Mechanisms in Local and Central Differential Privacy. CoRR abs/1911.08339 (2019) - [i41]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. IACR Cryptol. ePrint Arch. 2019: 245 (2019) - [i40]Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman:
Securely Sampling Biased Coins with Applications to Differential Privacy. IACR Cryptol. ePrint Arch. 2019: 823 (2019) - 2018
- [j10]Foto N. Afrati, Shantanu Sharma, Jonathan R. Ullman, Jeffrey D. Ullman:
Computing marginals using MapReduce. J. Comput. Syst. Sci. 94: 98-117 (2018) - [j9]Cynthia Dwork, Jonathan R. Ullman:
The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy. J. Priv. Confidentiality 8(1) (2018) - [j8]Mark Bun, Jonathan R. Ullman, Salil P. Vadhan:
Fingerprinting Codes and the Price of Approximate Differential Privacy. SIAM J. Comput. 47(5): 1888-1938 (2018) - [c36]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs:
Hardness of Non-interactive Differential Privacy from One-Way Functions. CRYPTO (1) 2018: 437-466 - [c35]Albert Cheu, Ravi Sundaram, Jonathan R. Ullman:
Skyline Identification in Multi-Arm Bandits. ISIT 2018: 1006-1010 - [c34]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. NeurIPS 2018: 2381-2390 - [c33]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [i39]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. CoRR abs/1802.07128 (2018) - [i38]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. CoRR abs/1805.00216 (2018) - [i37]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - [i36]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Mixnets. CoRR abs/1808.01394 (2018) - [i35]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) - [i34]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018) - 2017
- [j7]Jonathan R. Ullman:
Technical Perspective: Building a safety net for data reuse. Commun. ACM 60(4): 85 (2017) - [j6]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Antifolk Theorem for Large Repeated Games. ACM Trans. Economics and Comput. 5(2): 10:1-10:20 (2017) - [c32]Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan R. Ullman, Ali Vakilian, Anak Yodpinyanee:
Fractional Set Cover in the Streaming Model. APPROX-RANDOM 2017: 12:1-12:20 - [c31]Mitali Bafna, Jonathan R. Ullman:
The Price of Selection in Differential Privacy. COLT 2017: 151-168 - [c30]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. FOCS 2017: 552-563 - [c29]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. EC 2017: 519-536 - [c28]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. SODA 2017: 1306-1325 - [i33]Thomas Steinke, Jonathan R. Ullman:
Subgaussian Tail Bounds via Stability Arguments. CoRR abs/1701.03493 (2017) - [i32]Mitali Bafna, Jonathan R. Ullman:
The Price of Selection in Differential Privacy. CoRR abs/1702.02970 (2017) - [i31]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. CoRR abs/1704.03024 (2017) - [i30]Albert Cheu, Ravi Sundaram, Jonathan R. Ullman:
Skyline Identification in Multi-Armed Bandits. CoRR abs/1711.04213 (2017) - [i29]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs:
Hardness of Non-Interactive Differential Privacy from One-Way Functions. IACR Cryptol. ePrint Arch. 2017: 1107 (2017) - 2016
- [j5]Thomas Steinke, Jonathan R. Ullman:
Between Pure and Approximate Differential Privacy. J. Priv. Confidentiality 7(2) (2016) - [j4]Pavel Hubácek, Moni Naor, Jonathan R. Ullman:
When Can Limited Randomness Be Used in Repeated Games? Theory Comput. Syst. 59(4): 722-746 (2016) - [j3]Jonathan R. Ullman:
Answering n2+o(1) Counting Queries with Differential Privacy is Hard. SIAM J. Comput. 45(2): 473-496 (2016) - [c27]Foto N. Afrati, Shantanu Sharma, Jeffrey D. Ullman, Jonathan R. Ullman:
Computing Marginals Using MapReduce: Keynote talk paper. IDEAS 2016: 12-23 - [c26]Thomas Steinke, Jonathan R. Ullman:
Interactive fingerprinting codes and the hardness of preventing false discovery. ITA 2016: 1-41 - [c25]Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman:
Privacy Odometers and Filters: Pay-as-you-Go Composition. NIPS 2016: 1921-1929 - [c24]Edo Liberty, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman:
Space Lower Bounds for Itemset Frequency Sketches. PODS 2016: 441-454 - [c23]Jeffrey D. Ullman, Jonathan R. Ullman:
Some pairs problems. BeyondMR@SIGMOD 2016: 8 - [c22]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. STOC 2016: 949-962 - [c21]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [c20]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. TCC (B1) 2016: 659-689 - [r1]Jonathan R. Ullman:
Query Release via Online Learning. Encyclopedia of Algorithms 2016: 1716-1719 - [i28]Jeffrey D. Ullman, Jonathan R. Ullman:
Some Pairs Problems. CoRR abs/1602.01443 (2016) - [i27]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. CoRR abs/1604.04618 (2016) - [i26]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Salil P. Vadhan:
Privacy Odometers and Filters: Pay-as-you-Go Composition. CoRR abs/1605.08294 (2016) - [i25]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR abs/1607.05397 (2016) - [i24]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. CoRR abs/1607.06141 (2016) - [i23]Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan R. Ullman, Salil P. Vadhan:
PSI (Ψ): a Private data Sharing Interface. CoRR abs/1609.04340 (2016) - [i22]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. IACR Cryptol. ePrint Arch. 2016: 721 (2016) - 2015
- [j2]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. 14(1): 101-104 (2015) - [c19]Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. COLT 2015: 1588-1628 - [c18]Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan:
Robust Traceability from Trace Amounts. FOCS 2015: 650-669 - [c17]Jonathan R. Ullman:
Private Multiplicative Weights Beyond Linear Queries. PODS 2015: 303-312 - [c16]