Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds

Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a valid solution to some predefined problem at any point in time; the goal is to design an algorithm in which computing a solution to the updated input is done more efficiently than computing the solution from scratch. A dynamic algorithm against an adaptive adversary is required to be correct when the adversary chooses the next update after seeing the previous outputs of the algorithm. We obtain faster dynamic algorithms against an adaptive adversary and separation results between what is achievable in the oblivious vs. adaptive settings. To get these results we exploit techniques from differential privacy, cryptography, and adaptive data analysis. Our results are as follows. 1. We give a general reduction transforming a dynamic algorithm against an oblivious adversary to a dynamic algorithm robust against an adaptive adversary. This reduction maintains several copies of the oblivious algorithm and uses differential privacy to protect their random bits. Using this reduction we obtain dynamic algorithms against an adaptive adversary with improved update and query times for global minimum cut, all pairs distances, and all pairs effective resistance. 2. We further improve our update and query times by showing how to maintain a sparsifier over an expander decomposition that can be refreshed fast. This fast refresh enables it to be robust against what we call a blinking adversary that can observe the output of the algorithm only following refreshes. We believe that these techniques will prove useful for additional problems. 3. On the flip side, we specify dynamic problems that, assuming a random oracle, every dynamic algorithm that solves them against an adaptive adversary must be polynomially slower than a rather straightforward dynamic algorithm that solves them against an oblivious adversary. We first show a separation result for a search problem and then show a separation result for an estimation problem. In the latter case our separation result draws from lower bounds in adaptive data analysis.

Original languageEnglish
Title of host publicationSTOC 2022 - Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
EditorsStefano Leonardi, Anupam Gupta
PublisherAssociation for Computing Machinery
Pages1671-1684
Number of pages14
ISBN (Electronic)9781450392648
DOIs
StatePublished - 6 Sep 2022
Event54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022 - Rome, Italy
Duration: 20 Jun 202224 Jun 2022

Publication series

NameProceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)0737-8017

Conference

Conference54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022
Country/TerritoryItaly
CityRome
Period20/06/2224/06/22

Keywords

  • Dynamic algorithms
  • adaptive adversaries
  • differential privacy

ASJC Scopus subject areas

  • Software

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