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Characterizing History Independent Data Structures

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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.

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Authors and Affiliations

  1. Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA

    Jason D. Hartline

  2. Computing & Software Systems, University of Washington, 1900 Commerce St., Tacoma,WA 98402, USA

    Edwin S. Hong

  3. Department of Computer Science, State University of New York, Stony Brook, NY 11794, USA

    Alexander E. Mohr

  4. Department of Computer Science, University of Washington, Seattle, WA 98195, USA

    William R. Pentney & Emily C. Rocke

Authors
  1. Jason D. Hartline
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  2. Edwin S. Hong
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  3. Alexander E. Mohr
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  4. William R. Pentney
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  5. Emily C. Rocke
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Corresponding authors

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

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Hartline, J., Hong, E., Mohr, A. et al. Characterizing History Independent Data Structures. Algorithmica 42, 57–74 (2005). https://doi.org/10.1007/s00453-004-1140-z

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  • DOI: https://doi.org/10.1007/s00453-004-1140-z

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