Characterizing History Independent Data Structures
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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