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Scalable and Jointly Differentially Private Packing

Authors Zhiyi Huang, Xue Zhu

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LIPIcs.ICALP.2019.73.pdf
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Author Details

Zhiyi Huang
  • The University of Hong Kong
Xue Zhu
  • The University of Hong Kong

Funding

  • Huang, Zhiyi: This work is supported in by a RGC grant HKU17203717E.

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Zhiyi Huang and Xue Zhu. Scalable and Jointly Differentially Private Packing. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 73:1-73:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.ICALP.2019.73

Abstract

We introduce an (epsilon, delta)-jointly differentially private algorithm for packing problems. Our algorithm not only achieves the optimal trade-off between the privacy parameter epsilon and the minimum supply requirement (up to logarithmic factors), but is also scalable in the sense that the running time is linear in the number of agents n. Previous algorithms either run in cubic time in n, or require a minimum supply per resource that is sqrt{n} times larger than the best possible.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Packing and covering problems
Keywords
  • Joint differential privacy
  • packing
  • scalable algorithms

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