Document Open Access Logo

Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient

Authors Badih Ghazi, Ravi Kumar, Pasin Manurangsi

PDF

File

Thumbnail PDF
PDF
LIPIcs.ITC.2024.4.pdf
  • Filesize: 0.8 MB
  • 13 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Security and privacy
  • Security and privacy → Information-theoretic techniques
Keywords
  • Differential Privacy
  • Shuffle Model
  • Aggregation
  • Pure Differential Privacy

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

We obtain a new protocol for binary counting in the ε-DP_shuffle model with error O(1/ε) and expected communication Õ((log n)/ε) messages per user. Previous protocols incur either an error of O(1/ε^1.5) with O_ε(log n) messages per user (Ghazi et al., ITC 2020) or an error of O(1/ε) with O_ε(n²) messages per user (Cheu and Yan, TPDP 2022). Using the new protocol, we obtained improved ε-DP_shuffle protocols for real summation and histograms.

Cite As Get BibTex

Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. In 5th Conference on Information-Theoretic Cryptography (ITC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 304, pp. 4:1-4:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ITC.2024.4

Author Details

Badih Ghazi
  • Google Research, Mountain View, CA, USA
Ravi Kumar
  • Google Research, Mountain View, CA, USA
Pasin Manurangsi
  • Google Research, Bangkok, Thailand

References

  1. Victor Balcer and Albert Cheu. Separating local & shuffled differential privacy via histograms. In ITC, pages 1:1-1:14, 2020. Google Scholar
  2. Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim. The privacy blanket of the shuffle model. In CRYPTO, pages 638-667, 2019. Google Scholar
  3. Borja Balle, James Bell, Adrià Gascón, and Kobbi Nissim. Private summation in the multi-message shuffle model. In CCS, pages 657-676, 2020. Google Scholar
  4. Amos Beimel, Kobbi Nissim, and Eran Omri. Distributed private data analysis: Simultaneously solving how and what. In CRYPTO, pages 451-468, 2008. Google Scholar
  5. Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnés, and Bernhard Seefeld. Prochlo: Strong privacy for analytics in the crowd. In SOSP, pages 441-459, 2017. Google Scholar
  6. Mark Bun, Jelani Nelson, and Uri Stemmer. Heavy hitters and the structure of local privacy. TALG, 15(4):1-40, 2019. Google Scholar
  7. T.-H. Hubert Chan, Elaine Shi, and Dawn Song. Optimal lower bound for differentially private multi-party aggregation. In ESA, pages 277-288, 2012. Google Scholar
  8. Albert Cheu, Adam D. Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev. Distributed differential privacy via shuffling. In EUROCRYPT, pages 375-403, 2019. Google Scholar
  9. Albert Cheu and Chao Yan. Pure differential privacy from secure intermediaries. In TPDP, 2022. Google Scholar
  10. Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. Our data, ourselves: Privacy via distributed noise generation. In EUROCRYPT, pages 486-503, 2006. Google Scholar
  11. Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In TCC, pages 265-284, 2006. Google Scholar
  12. Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta. Amplification by shuffling: From local to central differential privacy via anonymity. In SODA, pages 2468-2479, 2019. Google Scholar
  13. Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant. Limiting privacy breaches in privacy preserving data mining. In PODS, pages 211-222, 2003. Google Scholar
  14. Quan Geng and Pramod Viswanath. The optimal noise-adding mechanism in differential privacy. IEEE Trans. Inf. Theory, 62(2):925-951, 2016. Google Scholar
  15. Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker. Pure differentially private summation from anonymous messages. In ITC, pages 15:1-15:23, 2020. Google Scholar
  16. Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, and Ameya Velingker. On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy. In EUROCRYPT, pages 463-488, 2021. Google Scholar
  17. Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. User-level differentially private learning via correlated sampling. In NeurIPS, pages 20172-20184, 2021. Google Scholar
  18. Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Rasmus Pagh. Private counting from anonymous messages: Near-optimal accuracy with vanishing communication overhead. In ICML, pages 3505-3514, 2020. Google Scholar
  19. Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, and Amer Sinha. Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message. In ICML, pages 3692-3701, 2021. Google Scholar
  20. Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, and Ameya Velingker. Private aggregation from fewer anonymous messages. In EUROCRYPT, pages 798-827, 2020. Google Scholar
  21. Arpita Ghosh, Tim Roughgarden, and Mukund Sundararajan. Universally utility-maximizing privacy mechanisms. In STOC, pages 351-360, 2009. Google Scholar
  22. Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Rashkodnikova, and Adam Smith. What can we learn privately? In FOCS, pages 531-540, 2008. Google Scholar
  23. Thomas Steinke and Jonathan Ullman. Between pure and approximate differential privacy. Journal of Privacy and Confidentiality, 7(2):3-22, 2016. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact