2017
DOI: 10.1109/tifs.2017.2721359
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Zipf’s Law in Passwords

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Cited by 355 publications
(254 citation statements)
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“…We note that in a passing comment, Juels and Rivest mention the possibility of using probabilistic password models (e.g., Weir et al's PCFG [32]) to build honeywords: "see Weir et al [32] for a presentation of an interesting alternative model for passwords, based on probabilistic context-free grammars" [21]. However, due to the Zipf-distribution nature of passwords [28], generating decoy passwords (according to a probabilistic password model or not) that are equally probable to the user's real password is inherently impossible (see Sec. VI).…”
Section: A Review Of Juels-rivest's Methodsmentioning
confidence: 99%
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“…We note that in a passing comment, Juels and Rivest mention the possibility of using probabilistic password models (e.g., Weir et al's PCFG [32]) to build honeywords: "see Weir et al [32] for a presentation of an interesting alternative model for passwords, based on probabilistic context-free grammars" [21]. However, due to the Zipf-distribution nature of passwords [28], generating decoy passwords (according to a probabilistic password model or not) that are equally probable to the user's real password is inherently impossible (see Sec. VI).…”
Section: A Review Of Juels-rivest's Methodsmentioning
confidence: 99%
“…They work on real-world password datasets, inherently different from the existing heuristic strategy (see [7], [14], [21]) that is mainly based on some specific counterexample passwords. Essentially, they make use of the fact that user-chosen passwords follow the Zipf's law [28], and ranks the sweetwords based on some known probability distribution, either normalized within a user or not. The probability distribution can be calculated from a leaked password dataset, or based on a probabilistic password model such as Markov [24].…”
Section: B Two Attacking Strategiesmentioning
confidence: 99%
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