Characterizing history independent data structures

Jason D. Hartline*, Edwin S. Hong, Alexander E. Mohr, William R. Pentney, Emily C. Rocke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We consider history independent data structures as proposed for study by Naor and Teague. In a history independent data structure, nothing can be learned from the memory representation of the data structure except for what is available from the abstract data structure. We show that for the most part, strong history independent data structures have canonical representations. We provide a natural alternative definition of strong history independence that is less restrictive than Naor and Teague and characterize how it restricts allowable representations. We also give a general formula for creating dynamically resizing history independent data structures and give a related impossibility result.

Original languageEnglish (US)
Pages (from-to)57-74
Number of pages18
JournalAlgorithmica (New York)
Volume42
Issue number1
DOIs
StatePublished - Mar 1 2005

Keywords

  • Algorithms
  • Data structures
  • History independence
  • Markov chains

ASJC Scopus subject areas

  • Computer Science(all)
  • Computer Science Applications
  • Applied Mathematics

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