Perfectly Secure Password Protocols in the Bounded Retrieval Model

Abstract

AbstractWe introduce a formal model, which we call the Bounded Retrieval Model, for the design and analysis of cryptographic protocols remaining secure against intruders that can retrieve a limited amount of parties’ private memory. The underlying model assumption on the intruders’ behavior is supported by real-life physical and logical considerations, such as the inherent superiority of a party’s local data bus over a remote intruder’s bandwidth-limited channel, or the detectability of voluminous resource access by any local intruder. More specifically, we assume a fixed upper bound on the amount of a party’s storage retrieved by the adversary. Our model could be considered a non-trivial variation of the well-studied Bounded Storage Model, which postulates a bound on the amount of storage available to an adversary attacking a given system.In this model we study perhaps the simplest among cryptographic tasks: user authentication via a password protocol. Specifically, we study the problem of constructing efficient password protocols that remain secure against offline dictionary attacks even when a large (but bounded) part of the storage of the server responsible for password verification is retrieved by an intruder through a remote or local connection. We show password protocols having satisfactory performance on both efficiency (in terms of the server’s running time) and provable security (making the offline dictionary attack not significantly stronger than the online attack). We also study the tradeoffs between efficiency, quantitative and qualitative security in these protocols. All our schemes achieve perfect security (security against computationally-unbounded adversaries). Our main schemes achieve the interesting efficiency property of the server’s lookup complexity being much smaller than the adversary’s retrieval bound.KeywordsHash FunctionSecurity ParameterSecret Share SchemeDictionary AttackStatic AdversaryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.