101. Password similarity using probabilistic data structures
- Author
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Andrea Melis, Davide Berardi, Franco Callegati, Marco Prandini, Davide Berardi, FRANCO CALLEGATI, Andrea Meli, and MARCO PRANDINI
- Subjects
Scheme (programming language) ,FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,Computer science ,hash functions ,Hash function ,0211 other engineering and technologies ,02 engineering and technology ,Reuse ,Computer security ,computer.software_genre ,Bloom Filter ,Similarity (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,password ,computer.programming_language ,Password ,021110 strategic, defence & security studies ,Probabilistic logic ,text analysis ,Bloom filter ,Data structure ,ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS ,hash function ,020201 artificial intelligence & image processing ,computer ,Cryptography and Security (cs.CR) ,Bloom Filters - Abstract
Passwords should be easy to remember, yet expiration policies mandate their frequent change. Caught in the crossfire between these conflicting requirements, users often adopt creative methods to perform slight variations over time. While easily fooling the most basic checks for similarity, these schemes lead to a substantial decrease in actual security, because leaked passwords, albeit expired, can be effectively exploited as seeds for crackers. This work describes an approach based on Bloom Filters to detect password similarity, which can be used to discourage password reuse habits. The proposed scheme intrinsically obfuscates the stored passwords to protect them in case of database leaks, and can be tuned to be resistant to common cryptanalytic techniques, making it suitable for usage on exposed systems.
- Published
- 2020