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A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

Authors Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

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Author Details

Idan Attias
  • Ben-Gurion University, Beer Sheva, Israel
Edith Cohen
  • Google Research, Mountain View, CA, USA
  • Tel Aviv University, Israel
Moshe Shechner
  • Tel Aviv University, Israel
Uri Stemmer
  • Tel Aviv University, Israel
  • Google Research, Herzliya, Israel

Funding

  • Attias, Idan: Partially supported by the Vatat Scholarship from the Israeli Council for Higher Education, Kreitman School of Advanced Graduate Studies, and the Lynn and William Frankel Center for Computer Science at Ben-Gurion University.
  • Cohen, Edith: Partially supported by the Israel Science Foundation (grant 1595/19).
  • Shechner, Moshe: Partially supported by the Israel Science Foundation (grant 1871/19).
  • Stemmer, Uri: Partially supported by the Israel Science Foundation (grant 1871/19) and by Len Blavatnik and the Blavatnik Family foundation.

Cite AsGet BibTex

Idan Attias, Edith Cohen, Moshe Shechner, and Uri Stemmer. A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 8:1-8:19, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ITCS.2023.8

Abstract

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. [NeurIPS 2020] and by Woodruff and Zhou [FOCS 2021]. These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the "best of both worlds", thereby solving a question left open by Woodruff and Zhou.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Streaming
  • adversarial robustness
  • differential privacy

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References

  1. Kook Jin Ahn, Sudipto Guha, and Andrew McGregor. Analyzing graph structure via linear measurements. In SODA, pages 459-467, 2012. URL: https://doi.org/10.1137/1.9781611973099.40.
  2. Kook Jin Ahn, Sudipto Guha, and Andrew McGregor. Graph sketches: sparsification, spanners, and subgraphs. In PODS, pages 5-14, 2012. URL: https://doi.org/10.1145/2213556.2213560.
  3. Noga Alon, Yossi Matias, and Mario Szegedy. The space complexity of approximating the frequency moments. Journal of Computer and system sciences, 58(1):137-147, 1999. Google Scholar
  4. Idan Attias, Edith Cohen, Moshe Shechner, and Uri Stemmer. A framework for adversarial streaming via differential privacy and difference estimators. CoRR, abs/2107.14527, 2021. URL: http://arxiv.org/abs/2107.14527.
  5. Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan R. Ullman. Algorithmic stability for adaptive data analysis. SIAM J. Comput., 50(3), 2021. Google Scholar
  6. Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, and Uri Stemmer. Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. In STOC, pages 1671-1684. ACM, 2022. Google Scholar
  7. Omri Ben-Eliezer, Rajesh Jayaram, David P Woodruff, and Eylon Yogev. A framework for adversarially robust streaming algorithms. In Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pages 63-80, 2020. Google Scholar
  8. Omri Ben-Eliezer and Eylon Yogev. The adversarial robustness of sampling. CoRR, abs/1906.11327, 2019. URL: http://arxiv.org/abs/1906.11327.
  9. Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, and Samson Zhou. Adversarial robustness of streaming algorithms through importance sampling. CoRR, abs/2106.14952, 2021. Google Scholar
  10. Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, and Uri Stemmer. On the robustness of countsketch to adaptive inputs. In ICML, volume 162 of Proceedings of Machine Learning Research, pages 4112-4140. PMLR, 2022. Google Scholar
  11. Edith Cohen, Jelani Nelson, Tamás Sarlós, and Uri Stemmer. Tricking the hashing trick: A tight lower bound on the robustness of countsketch to adaptive inputs. CoRR, abs/2207.00956, 2022. Google Scholar
  12. Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth. Preserving statistical validity in adaptive data analysis. In STOC, pages 117-126. ACM, 2015. Google Scholar
  13. Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. Google Scholar
  14. A. C. Gilbert, B. Hemenway, A. Rudra, M. J. Strauss, and M. Wootters. Recovering simple signals. In 2012 Information Theory and Applications Workshop, pages 382-391, 2012. Google Scholar
  15. A. C. Gilbert, B. Hemenway, M. J. Strauss, D. P. Woodruff, and M. Wootters. Reusable low-error compressive sampling schemes through privacy. In 2012 IEEE Statistical Signal Processing Workshop (SSP), pages 536-539, 2012. Google Scholar
  16. Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. Adaptive machine unlearning. arXiv preprint, 2021. URL: http://arxiv.org/abs/2106.04378.
  17. Moritz Hardt and Jonathan R. Ullman. Preventing false discovery in interactive data analysis is hard. In FOCS, pages 454-463. IEEE Computer Society, 2014. Google Scholar
  18. Moritz Hardt and David P. Woodruff. How robust are linear sketches to adaptive inputs? In STOC, pages 121-130, 2013. Google Scholar
  19. Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, and Uri Stemmer. Adversarially robust streaming algorithms via differential privacy. In NeurIPS, 2020. Google Scholar
  20. Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. A new analysis of differential privacy’s generalization guarantees. In ITCS, volume 151 of LIPIcs, pages 31:1-31:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. Google Scholar
  21. Haim Kaplan, Yishay Mansour, Kobbi Nissim, and Uri Stemmer. Separating adaptive streaming from oblivious streaming using the bounded storage model. In CRYPTO 2021, 2021. URL: http://arxiv.org/abs/2101.10836.
  22. Aryeh Kontorovich, Menachem Sadigurschi, and Uri Stemmer. Adaptive data analysis with correlated observations. In ICML, volume 162 of Proceedings of Machine Learning Research, pages 11483-11498. PMLR, 2022. Google Scholar
  23. Ilya Mironov, Moni Naor, and Gil Segev. Sketching in adversarial environments. SIAM J. Comput., 40(6):1845-1870, 2011. URL: https://doi.org/10.1137/080733772.
  24. Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman. The limits of post-selection generalization. In NeurIPS, pages 6402-6411, 2018. Google Scholar
  25. Kobbi Nissim and Uri Stemmer. Concentration bounds for high sensitivity functions through differential privacy. J. Priv. Confidentiality, 9(1), 2019. Google Scholar
  26. Moshe Shenfeld and Katrina Ligett. A necessary and sufficient stability notion for adaptive generalization. In NeurIPS, pages 11481-11490, 2019. Google Scholar
  27. Moshe Shenfeld and Katrina Ligett. Generalization in the face of adaptivity: A bayesian perspective. CoRR, abs/2106.10761, 2021. URL: http://arxiv.org/abs/2106.10761.
  28. Thomas Steinke and Jonathan R. Ullman. Interactive fingerprinting codes and the hardness of preventing false discovery. In COLT, volume 40 of JMLR Workshop and Conference Proceedings, pages 1588-1628. JMLR.org, 2015. Google Scholar
  29. David P. Woodruff and Samson Zhou. Tight bounds for adversarially robust streams and sliding windows via difference estimators. In FOCS, 2021. Google Scholar
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