Characterizing History Independent Data Structures

We consider history independent data structures as proposed for study by Teague and Naor [3]. In a history independent data structure, nothing can be learned from the 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 also provide a natural less restrictive definition of strong history independence 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.