Thomas Steinke

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 88

Top Venues

CoRR

NeurIPS

COLT

ICML

Publications

Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
Privacy Amplification for Matrix Mechanisms. ICLR (2024)
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS (2024)
Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman
Private Geometric Median. CoRR (2024)
Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
Stealing Part of a Production Language Model. CoRR (2024)
Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. ICLR (2024)
Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR (2024)
Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition. CoRR (2024)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang
Why Is Public Pretraining Necessary for Private Model Training? ICML (2023)
Alexander Knop, Thomas Steinke
Counting Distinct Elements Under Person-Level Differential Privacy. CoRR (2023)
Thomas Steinke, Milad Nasr, Matthew Jagielski
Privacy Auditing with One (1) Training Run. CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Thakurta, Lun Wang
Why Is Public Pretraining Necessary for Private Model Training? CoRR (2023)
Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS (2023)
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Privacy Amplification for Matrix Mechanisms. CoRR (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. CoRR (2023)
Thomas Steinke, Milad Nasr, Matthew Jagielski
Privacy Auditing with One (1) Training Run. NeurIPS (2023)
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis
Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium (2023)
Thomas Steinke, Alexander Knop
Counting Distinct Elements Under Person-Level Differential Privacy. NeurIPS (2023)
Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR (2023)
Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek
Differentially Private Stream Processing at Scale. CoRR (2023)
Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR (2023)
Gautam Kamath, Argyris Mouzakis, Matthew Regehr, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke
Algorithms with More Granular Differential Privacy Guarantees. ITCS (2023)
Thomas Steinke
Composition of Differential Privacy & Privacy Amplification by Subsampling. CoRR (2022)
Nicolas Papernot, Thomas Steinke
Hyperparameter Tuning with Renyi Differential Privacy. ICLR (2022)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke
Algorithms with More Granular Differential Privacy Guarantees. CoRR (2022)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT (2022)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
Discrete Gaussian for Differential Privacy. J. Priv. Confidentiality 12 (1) (2022)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. ICML (2022)
Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini
Debugging Differential Privacy: A Case Study for Privacy Auditing. 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)
Peter Kairouz, Ziyu Liu, Thomas Steinke
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. ICML (2021)
Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS (2021)
Nicolas Papernot, Thomas Steinke
Hyperparameter Tuning with Renyi Differential Privacy. CoRR (2021)
Peter Kairouz, Ziyu Liu, Thomas Steinke
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. CoRR (2021)
Vikrant Singhal, Thomas Steinke
Privately Learning Subspaces. NeurIPS (2021)
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. CoRR (2021)
Vikrant Singhal, Thomas Steinke
Privately Learning Subspaces. CoRR (2021)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. IEEE Trans. Inf. Theory 67 (3) (2021)
Peter Grünwald, Thomas Steinke, Lydia Zakynthinou
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. COLT (2021)
Peter Grünwald, Thomas Steinke, Lydia Zakynthinou
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. CoRR (2021)
Zeyu Ding, Daniel Kifer, Sayed M. Saghaian N. E., Thomas Steinke, Yuxin Wang, Yingtai Xiao, Danfeng Zhang
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise. CoRR (2021)
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. ICML (2021)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. CoRR (2021)
Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR (2021)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
The Discrete Gaussian for Differential Privacy. NeurIPS (2020)
Thomas Steinke
Multi-Central Differential Privacy. CoRR (2020)
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
New Oracle-Efficient Algorithms for Private Synthetic Data Release. ICML (2020)
Thomas Steinke, Lydia Zakynthinou
Reasoning About Generalization via Conditional Mutual Information. CoRR (2020)
Thomas Steinke, Lydia Zakynthinou
Reasoning About Generalization via Conditional Mutual Information. COLT (2020)
Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
New Oracle-Efficient Algorithms for Private Synthetic Data Release. CoRR (2020)
Thomas Steinke, Lydia Zakynthinou
Open Problem: Information Complexity of VC Learning. COLT (2020)
Clément L. Canonne, Gautam Kamath, Thomas Steinke
The Discrete Gaussian for Differential Privacy. CoRR (2020)
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). Inform. Spektrum 42 (5) (2019)
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)
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou
A Hybrid Approach to Privacy-Preserving Federated Learning. AISec@CCS (2019)
Jaroslaw Blasiok, Mark Bun, Aleksandar Nikolov, Thomas Steinke
Towards Instance-Optimal Private Query Release. SODA (2019)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. NeurIPS (2019)
Mark Bun, Thomas Steinke
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. CoRR (2019)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. CoRR (2019)
Mark Bun, Thomas Steinke
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. NeurIPS (2019)
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)
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang
A Hybrid Approach to Privacy-Preserving Federated Learning. CoRR (2018)
Mark Bun, Cynthia Dwork, Guy N. Rothblum, Thomas Steinke
Composable and versatile privacy via truncated CDP. STOC (2018)
Vitaly Feldman, Thomas Steinke
Calibrating Noise to Variance in Adaptive Data Analysis. COLT (2018)
Jaroslaw Blasiok, Mark Bun, Aleksandar Nikolov, Thomas Steinke
Towards Instance-Optimal Private Query Release. CoRR (2018)
Vitaly Feldman, Thomas Steinke
Generalization for Adaptively-chosen Estimators via Stable Median. CoRR (2017)
Mark Bun, Thomas Steinke, Jonathan R. Ullman
Make Up Your Mind: The Price of Online Queries in Differential Privacy. SODA (2017)
Vitaly Feldman, Thomas Steinke
Generalization for Adaptively-chosen Estimators via Stable Median. COLT (2017)
Thomas Steinke, Jonathan R. Ullman
Tight Lower Bounds for Differentially Private Selection. FOCS (2017)
Thomas Steinke, Jonathan R. Ullman
Subgaussian Tail Bounds via Stability Arguments. CoRR (2017)
Vitaly Feldman, Thomas Steinke
Calibrating Noise to Variance in Adaptive Data Analysis. CoRR (2017)
Thomas Steinke, Jonathan R. Ullman
Tight Lower Bounds for Differentially Private Selection. CoRR (2017)
Thomas Steinke, Salil P. Vadhan, Andrew Wan
Pseudorandomness and Fourier-Growth Bounds for Width-3 Branching Programs. Theory Comput. 13 (1) (2017)
Thomas Steinke, Jonathan R. Ullman
Interactive fingerprinting codes and the hardness of preventing false discovery. ITA (2016)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic stability for adaptive data analysis. STOC (2016)
Mark Bun, Thomas Steinke
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. CoRR (2016)
Mark Bun, Thomas Steinke, Jonathan R. Ullman
Make Up Your Mind: The Price of Online Queries in Differential Privacy. CoRR (2016)
Mark Bun, Thomas Steinke
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. TCC (B1) (2016)
Mark Bun, Thomas Steinke
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. IACR Cryptol. ePrint Arch. 2016 (2016)
Thomas Steinke, Jonathan R. Ullman
Between Pure and Approximate Differential Privacy. J. Priv. Confidentiality 7 (2) (2016)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. CoRR (2015)
Sitan Chen, Thomas Steinke, Salil P. Vadhan
Pseudorandomness for Read-Once, Constant-Depth Circuits. CoRR (2015)
Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan
Robust Traceability from Trace Amounts. FOCS (2015)
Mark Bun, Thomas Steinke
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. APPROX-RANDOM (2015)
Thomas Steinke, Jonathan R. Ullman
Between Pure and Approximate Differential Privacy. CoRR (2015)
Thomas Steinke, Jonathan R. Ullman
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. COLT (2015)
Thomas Steinke
Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs. Electron. Colloquium Comput. Complex. 21 (2014)
Varun Kanade, Thomas Steinke
Learning Hurdles for Sleeping Experts. ACM Trans. Comput. Theory 6 (3) (2014)
Thomas Steinke, Salil P. Vadhan, Andrew Wan
Pseudorandomness and Fourier Growth Bounds for Width-3 Branching Programs. APPROX-RANDOM (2014)
Thomas Steinke, Jonathan R. Ullman
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. CoRR (2014)
Thomas Steinke, Salil P. Vadhan, Andrew Wan
Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs. CoRR (2014)
Mark Bun, Thomas Steinke
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. CoRR (2014)
Mark Bun, Thomas Steinke
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. Electron. Colloquium Comput. Complex. 21 (2014)
Omer Reingold, Thomas Steinke, Salil P. Vadhan
Pseudorandomness for Regular Branching Programs via Fourier Analysis. CoRR (2013)
Raazesh Sainudiin, Thomas Steinke
A Rigorous Extension of the Schönhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic. Reliab. Comput. 18 (2013)
Omer Reingold, Thomas Steinke, Salil P. Vadhan
Pseudorandomness for Regular Branching Programs via Fourier Analysis. APPROX-RANDOM (2013)
Omer Reingold, Thomas Steinke, Salil P. Vadhan
Pseudorandomness for Regular Branching Programs via Fourier Analysis. Electron. Colloquium Comput. Complex. 20 (2013)
Thomas Steinke
Pseudorandomness for Permutation Branching Programs Without the Group Theory. Electron. Colloquium Comput. Complex. 19 (2012)

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Publication Activity (10 Years)

Top Topics

Top Venues

Thomas Steinke