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History Independence as an Algorithmic Tool (Invited Talk)

Author William Kuszmaul

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Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
Keywords
  • Data Structures
  • History Independence
  • List-Labeling
  • Randomized Algorithms

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Abstract

In this talk, I’ll argue that history independence - a widely-studied privacy property in the data-structures literature - is actually a powerful algorithmic tool, not just for privacy, but for building faster and better data structures. Along the way, I’ll discuss a recent sequence of results on the list-labeling problem, where history independence played a pivotal role in overturning a 30-year-old conjecture.

Cite As Get BibTex

William Kuszmaul. History Independence as an Algorithmic Tool (Invited Talk). In 34th Annual European Symposium on Algorithms (ESA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 388, p. 1:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ESA.2026.1

Author Details

William Kuszmaul
  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA

References

  1. Nikhil Bansal and William Kuszmaul. Balanced allocations: The heavily loaded case with deletions. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 801-812. IEEE, 2022. URL: https://doi.org/10.1109/FOCS54457.2022.00081.
  2. Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Cliff Stein, and Madhu Sudan. Fully dynamic maximal independent set with polylogarithmic update time. In 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), pages 382-405. IEEE, 2019. URL: https://doi.org/10.1109/FOCS.2019.00032.
  3. Michael A Bender, Alex Conway, Martín Farach-Colton, Hanna Komlós, William Kuszmaul, and Nicole Wein. Online list labeling: Breaking the log 2 n barrier. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 980-990. IEEE, 2022. URL: https://doi.org/10.1109/FOCS54457.2022.00096.
  4. Michael A Bender, William Kuszmaul, Elaine Shi, and Rose Silver. History-independent load balancing. In Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1097-1127. SIAM, 2026. URL: https://doi.org/10.1137/1.9781611978971.44.
  5. Michael A Bender, William Kuszmaul, and Renfei Zhou. Tight bounds for classical open addressing. In 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), pages 636-657. IEEE, 2024. URL: https://doi.org/10.1109/FOCS61266.2024.00047.
  6. Pedro Celis, Per-Ake Larson, and J Ian Munro. Robin hood hashing. In 26th annual symposium on foundations of computer science (sfcs 1985), pages 281-288. IEEE, 1985. Google Scholar
  7. Shiri Chechik and Tianyi Zhang. Fully dynamic maximal independent set in expected poly-log update time. In 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), pages 370-381. IEEE, 2019. URL: https://doi.org/10.1109/FOCS.2019.00031.
  8. Rathish Das and William Kuszmaul. History-independent maximal matchings can be surprisingly efficient, and lead to better worst-case guarantees. In Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 3999-4022. SIAM, 2026. URL: https://doi.org/10.1137/1.9781611978971.147.
  9. William Kuszmaul. Strongly history-independent storage allocation: New upper and lower bounds. In 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), pages 1822-1841. IEEE, 2023. URL: https://doi.org/10.1109/FOCS57990.2023.00111.
  10. Moni Naor and Vanessa Teague. Anti-persistence: History independent data structures. In Proceedings of the thirty-third annual ACM symposium on Theory of computing, pages 492-501, 2001. URL: https://doi.org/10.1145/380752.380844.
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