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ORCIDThomas Steinke 0002
Person information
- affiliation: Google, Mountain View, CA, USA
- affiliation: IBM Almaden Research Center, San Jose, CA, USA
- affiliation (PhD 2016): Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA
- affiliation: University of Canterbury, Department of Mathematics and Statistics, Christchurch, New Zealand
Other persons with the same name
- Thomas Steinke 0001
![[0000-0002-0338-8042]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-0338-8042")
— Zuse Institute Berlin (ZIB), Germany - Thomas Steinke 0003 — California Polytechnic State University, San Jose, CA, USA
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Journal Articles
- 2024
[j10]Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Eidan Cohen, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. Proc. VLDB Endow. 17(12): 4145-4158 (2024)- 2022
- [j9]Clément L. Canonne, Gautam Kamath, Thomas Steinke:
Discrete Gaussian for Differential Privacy. J. Priv. Confidentiality 12(1) (2022) - 2021
- [j8]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) - [j7]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. IEEE Trans. Inf. Theory 67(3): 1981-2000 (2021) - 2019
- [j6]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): 356-357 (2019) - [j5]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) - 2017
- [j4]Thomas Steinke, Salil P. Vadhan, Andrew Wan:
Pseudorandomness and Fourier-Growth Bounds for Width-3 Branching Programs. Theory Comput. 13(1): 1-50 (2017) - 2016
- [j3]Thomas Steinke, Jonathan R. Ullman:
Between Pure and Approximate Differential Privacy. J. Priv. Confidentiality 7(2) (2016) - 2014
- [j2]Varun Kanade, Thomas Steinke:
Learning Hurdles for Sleeping Experts. ACM Trans. Comput. Theory 6(3): 11:1-11:16 (2014) - 2013
- [j1]Raazesh Sainudiin, Thomas Steinke:
A Rigorous Extension of the Schönhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic. Reliab. Comput. 18: 97-116 (2013)
Conference and Workshop Papers
- 2024
- [c43]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 - [c42]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Privacy Amplification for Matrix Mechanisms. ICLR 2024 - [c41]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. ICML 2024 - [c40]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 - 2023
- [c39]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: 10611-10627 - [c38]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. ITCS 2023: 54:1-54:24 - [c37]Thomas Steinke, Alexander Knop:
Counting Distinct Elements Under Person-Level Differential Privacy. NeurIPS 2023 - [c36]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. NeurIPS 2023 - [c35]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. NeurIPS 2023 - [c34]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: 1631-1648 - 2022
- [c33]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT 2022: 544-572 - [c32]Nicolas Papernot, Thomas Steinke:
Hyperparameter Tuning with Renyi Differential Privacy. ICLR 2022 - [c31]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: 517-535 - 2021
- [c30]Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta:
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS 2021: 2638-2646 - [c29]Peter Grünwald, Thomas Steinke, Lydia Zakynthinou:
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. COLT 2021: 2217-2247 - [c28]Peter Kairouz, Ziyu Liu, Thomas Steinke:
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. ICML 2021: 5201-5212 - [c27]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. ICML 2021: 6968-6977 - [c26]Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. NeurIPS 2021: 1312-1324 - 2020
- [c25]Thomas Steinke, Lydia Zakynthinou:
Reasoning About Generalization via Conditional Mutual Information. COLT 2020: 3437-3452 - [c24]Thomas Steinke, Lydia Zakynthinou:
Open Problem: Information Complexity of VC Learning. COLT 2020: 3857-3863 - [c23]Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu:
New Oracle-Efficient Algorithms for Private Synthetic Data Release. ICML 2020: 9765-9774 - [c22]Clément L. Canonne, Gautam Kamath, Thomas Steinke:
The Discrete Gaussian for Differential Privacy. NeurIPS 2020 - 2019
- [c21]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: 1-11 - [c20]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. NeurIPS 2019: 156-167 - [c19]Mark Bun, Thomas Steinke:
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. NeurIPS 2019: 181-191 - [c18]Jaroslaw Blasiok, Mark Bun, Aleksandar Nikolov, Thomas Steinke:
Towards Instance-Optimal Private Query Release. SODA 2019: 2480-2497 - 2018
- [c17]Vitaly Feldman, Thomas Steinke:
Calibrating Noise to Variance in Adaptive Data Analysis. COLT 2018: 535-544 - [c16]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [c15]Mark Bun, Cynthia Dwork, Guy N. Rothblum, Thomas Steinke:
Composable and versatile privacy via truncated CDP. STOC 2018: 74-86 - 2017
- [c14]Vitaly Feldman, Thomas Steinke:
Generalization for Adaptively-chosen Estimators via Stable Median. COLT 2017: 728-757 - [c13]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. FOCS 2017: 552-563 - [c12]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. SODA 2017: 1306-1325 - 2016
- [c11]Thomas Steinke, Jonathan R. Ullman:
Interactive fingerprinting codes and the hardness of preventing false discovery. ITA 2016: 1-41 - [c10]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [c9]Mark Bun, Thomas Steinke:
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. TCC (B1) 2016: 635-658 - 2015
- [c8]Mark Bun, Thomas Steinke:
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. APPROX-RANDOM 2015: 625-644 - [c7]Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. COLT 2015: 1588-1628 - [c6]Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan:
Robust Traceability from Trace Amounts. FOCS 2015: 650-669 - 2014
- [c5]Thomas Steinke, Salil P. Vadhan, Andrew Wan:
Pseudorandomness and Fourier Growth Bounds for Width-3 Branching Programs. APPROX-RANDOM 2014: 885-899 - 2013
- [c4]Omer Reingold, Thomas Steinke, Salil P. Vadhan:
Pseudorandomness for Regular Branching Programs via Fourier Analysis. APPROX-RANDOM 2013: 655-670 - 2012
- [c3]Justin Thaler, Michael Mitzenmacher, Thomas Steinke:
Hierarchical Heavy Hitters with the Space Saving Algorithm. ALENEX 2012: 160-174 - [c2]Varun Kanade, Thomas Steinke:
Learning hurdles for sleeping experts. ITCS 2012: 11-18 - 2010
- [c1]Thomas Steinke, Raazesh Sainudiin:
A Rigorous Extension of the Schönhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic. CCA 2010: 151-159
Informal and Other Publications
- 2024
- [i54]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 abs/2403.06634 (2024) - [i53]Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta:
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR abs/2404.16706 (2024) - [i52]Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke:
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition. CoRR abs/2405.20769 (2024) - [i51]Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman:
Private Geometric Median. CoRR abs/2406.07407 (2024) - 2023
- [i50]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) - [i49]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. CoRR abs/2302.07956 (2023) - [i48]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 abs/2302.09483 (2023) - [i47]Bing Zhang, Vadym Doroshenko, Peter Kairouz, Thomas Steinke, Abhradeep Thakurta, Ziyin Ma, Himani Apte, Jodi Spacek:
Differentially Private Stream Processing at Scale. CoRR abs/2303.18086 (2023) - [i46]Thomas Steinke, Milad Nasr, Matthew Jagielski:
Privacy Auditing with One (1) Training Run. CoRR abs/2305.08846 (2023) - [i45]Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Thakurta:
Faster Differentially Private Convex Optimization via Second-Order Methods. CoRR abs/2305.13209 (2023) - [i44]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR abs/2305.13440 (2023) - [i43]Alexander Knop, Thomas Steinke:
Counting Distinct Elements Under Person-Level Differential Privacy. CoRR abs/2308.12947 (2023) - [i42]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 abs/2310.06771 (2023) - [i41]Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Privacy Amplification for Matrix Mechanisms. CoRR abs/2310.15526 (2023) - 2022
- [i40]Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini:
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR abs/2202.12219 (2022) - [i39]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. CoRR abs/2209.04053 (2022) - [i38]Thomas Steinke:
Composition of Differential Privacy & Privacy Amplification by Subsampling. CoRR abs/2210.00597 (2022) - 2021
- [i37]Peter Kairouz, Ziyu Liu, Thomas Steinke:
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. CoRR abs/2102.06387 (2021) - [i36]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) - [i35]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 abs/2105.07260 (2021) - [i34]Vikrant Singhal, Thomas Steinke:
Privately Learning Subspaces. CoRR abs/2106.00001 (2021) - [i33]Peter Grünwald, Thomas Steinke, Lydia Zakynthinou:
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. CoRR abs/2106.09683 (2021) - [i32]Nicolas Papernot, Thomas Steinke:
Hyperparameter Tuning with Renyi Differential Privacy. CoRR abs/2110.03620 (2021) - [i31]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) - [i30]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 abs/2112.00193 (2021) - 2020
- [i29]Thomas Steinke, Lydia Zakynthinou:
Reasoning About Generalization via Conditional Mutual Information. CoRR abs/2001.09122 (2020) - [i28]Clément L. Canonne, Gautam Kamath, Thomas Steinke:
The Discrete Gaussian for Differential Privacy. CoRR abs/2004.00010 (2020) - [i27]Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu:
New Oracle-Efficient Algorithms for Private Synthetic Data Release. CoRR abs/2007.05453 (2020) - [i26]Thomas Steinke:
Multi-Central Differential Privacy. CoRR abs/2009.05401 (2020) - 2019
- [i25]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. CoRR abs/1905.13229 (2019) - [i24]Mark Bun, Thomas Steinke:
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. CoRR abs/1906.02830 (2019) - 2018
- [i23]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - [i22]Jaroslaw Blasiok, Mark Bun, Aleksandar Nikolov, Thomas Steinke:
Towards Instance-Optimal Private Query Release. CoRR abs/1811.03763 (2018) - [i21]Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang:
A Hybrid Approach to Privacy-Preserving Federated Learning. CoRR abs/1812.03224 (2018) - 2017
- [i20]Thomas Steinke, Jonathan R. Ullman:
Subgaussian Tail Bounds via Stability Arguments. CoRR abs/1701.03493 (2017) - [i19]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. CoRR abs/1704.03024 (2017) - [i18]Vitaly Feldman, Thomas Steinke:
Generalization for Adaptively-chosen Estimators via Stable Median. CoRR abs/1706.05069 (2017) - [i17]Vitaly Feldman, Thomas Steinke:
Calibrating Noise to Variance in Adaptive Data Analysis. CoRR abs/1712.07196 (2017) - 2016
- [i16]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. CoRR abs/1604.04618 (2016) - [i15]Mark Bun, Thomas Steinke:
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. CoRR abs/1605.02065 (2016) - [i14]Mark Bun, Thomas Steinke:
Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. IACR Cryptol. ePrint Arch. 2016: 816 (2016) - 2015
- [i13]Thomas Steinke, Jonathan R. Ullman:
Between Pure and Approximate Differential Privacy. CoRR abs/1501.06095 (2015) - [i12]Sitan Chen, Thomas Steinke, Salil P. Vadhan:
Pseudorandomness for Read-Once, Constant-Depth Circuits. CoRR abs/1504.04675 (2015) - [i11]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) - 2014
- [i10]Thomas Steinke, Salil P. Vadhan, Andrew Wan:
Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs. CoRR abs/1405.7028 (2014) - [i9]Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. CoRR abs/1410.1228 (2014) - [i8]Mark Bun, Thomas Steinke:
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. CoRR abs/1412.2457 (2014) - [i7]Mark Bun, Thomas Steinke:
Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. Electron. Colloquium Comput. Complex. TR14 (2014) - [i6]Thomas Steinke:
Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs. Electron. Colloquium Comput. Complex. TR14 (2014) - 2013
- [i5]Omer Reingold, Thomas Steinke, Salil P. Vadhan:
Pseudorandomness for Regular Branching Programs via Fourier Analysis. CoRR abs/1306.3004 (2013) - [i4]Omer Reingold, Thomas Steinke, Salil P. Vadhan:
Pseudorandomness for Regular Branching Programs via Fourier Analysis. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [i3]Thomas Steinke:
Pseudorandomness for Permutation Branching Programs Without the Group Theory. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [i2]Michael Mitzenmacher, Thomas Steinke, Justin Thaler:
Hierarchical Heavy Hitters with the Space Saving Algorithm. CoRR abs/1102.5540 (2011) - [i1]Varun Kanade, Thomas Steinke:
Learning Hurdles for Sleeping Experts. Electron. Colloquium Comput. Complex. TR11 (2011)
Coauthor Index
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