Scalable and Jointly Differentially Private Packing

Authors Zhiyi Huang, Xue Zhu

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

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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)


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
  • Joint differential privacy
  • packing
  • scalable algorithms


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