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Password Similarity Using Probabilistic Data Structures

Dipartimento di Informatica Scienza e Ingegneria (DISI), Università di Bologna, 40126 Bologna, Italy
Author to whom correspondence should be addressed.
J. Cybersecur. Priv. 2021, 1(1), 78-92;
Received: 3 November 2020 / Revised: 20 December 2020 / Accepted: 27 December 2020 / Published: 31 December 2020
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. View Full-Text
Keywords: password; Bloom Filters; hash functions; text analysis password; Bloom Filters; hash functions; text analysis
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MDPI and ACS Style

Berardi, D.; Callegati, F.; Melis, A.; Prandini, M. Password Similarity Using Probabilistic Data Structures. J. Cybersecur. Priv. 2021, 1, 78-92.

AMA Style

Berardi D, Callegati F, Melis A, Prandini M. Password Similarity Using Probabilistic Data Structures. Journal of Cybersecurity and Privacy. 2021; 1(1):78-92.

Chicago/Turabian Style

Berardi, Davide; Callegati, Franco; Melis, Andrea; Prandini, Marco. 2021. "Password Similarity Using Probabilistic Data Structures" J. Cybersecur. Priv. 1, no. 1: 78-92.

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