Titlebook: Lehrbuch der Psychiatrie; Eugen Bleuler Book 1937Latest edition Springer-Verlag Berlin Heidelberg 1937 Gesundheit.Lehrbuch.Medizin.Psychia

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Eugen Bleulerruct the differential-ML (machine learning) distinguisher by combining the traditional differential distinguisher and the machine learning based differential distinguisher, which breaks the limit of the ML differential distinguisher on the number of attack rounds. However, the results obtained based

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Eugen Bleulerer for the cipher . consisted of the rotational attack and the linear attack together with some intermediate variables. Then a key recovery technique is introduced with which we can recover some bits of the last whitening key in the related-key scenario. To decrease data complexity of our attack, we

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Eugen Bleulern attack resistance..Firstly, we focus on the Even-Mansour structure hash function with two iterations. Basing on the permutation used in the Even-Mansour structure hash function we construct two new functions . and ., and find the partial invariables of input-output in one function .. Then using th

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Eugen Bleuleradversaries as follows: Even if an adversary breaks the scheme’s underlying computational hard problem and hence forges a signature, then with overwhelming probability the signer is able to prove that a forgery has occurred (i.e. that the underlying hard problem has been broken). Although there is a

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Eugen Bleulersed extensively in cloud storage. When combining with confidentiality, dedupliation will become problematic as encryption performed by different users may differentiate identical data. MLE (Message-Locked Encryption) is thus utilized to derive the same encryption key for the identical data. As keys