Attaque de collisions

Résumé

En cryptographie, une attaque de collisions est une attaque sur une fonction de hachage cryptographique qui tente de trouver deux entrées de cette fonction qui produisent le même résultat (appelé valeur de hachage), c'est-à-dire qui résultent en une collision. Dans une attaque de collisions, contrairement à une (), la valeur de hachage n'est pas précisée. Il existe deux types principaux d'attaques de collisions :

l'attaque de collisions classique : cette attaque consiste à trouver deux messages m1 et m2 différents, tels que hachage (m1) = hachage (m2) ;
l'attaque de collisions avec préfixes choisis : étant donné deux préfixes différents P1 et P2, cette attaque consiste à trouver deux suffixes S1 et S2 tels que hachage (P1 ∥ S1) = hachage ( P2 ∥ S2) (où ∥ est l'opération de concaténation).

Attaque de collisions avec préfixes choisis Une variante de l'attaque de collisions classique est une attaque de collisions avec préfixes choisis qui est spécifique aux fonctions de

Source officielle

https://fr.wikipedia.org/wiki/Attaque_de_collisions

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This paper presents improved collision attacks on round-reduced variants of the hash function CubeHash, one of the SHA-3 second round candidates. We apply two methods for finding linear differential trails that lead to lower estimated attack complexities when used within the framework introduced by Brier,.Khazaei, Meier and Peyrin at ASIA-CRYPT 2009. The first method yields trails that are relatively dense at the beginning and sparse towards the end. In combination with the condition function concept; such trails lead to much faster collision attacks. We demonstrate this by providing a. real collision for CubeHash-5/96. The second method randomizes the search for highly probable linear differential trails and leads to significantly better attacks for up to eight rounds.

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2010

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Knudsen and Preneel (Asiacrypt'96 and Crypto'97) introduced a hash function design in which a linear error-correcting code is used to build a wide-pipe compression function from underlying blockciphers operating in Davies-Meyer mode. Their main design goal was to deliver compression functions with collision resistance up to, and even beyond, the block size of the underlying blockciphers. In this paper, we present new collision-finding attacks against these compression functions using the ideas of an unpublished work of Watanabe and the preimage attack of Ozen, Shrimpton, and Stain (FSE'10). In brief, our best attack has a time complexity strictly smaller than the block-size for all but two of the parameter sets. Consequently, the time complexity lower bound proven by Knudsen and Preneel is incorrect and the compression functions do not achieve the security level they were designed for.

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2010

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, ,

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis. The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., "steal") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.

Usenix Assoc2016

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