Public Channel Cryptography by Synchronization of Neural Networks and Chaotic Maps

Mislovaty, Rachel; Klein, Einat; Kanter, Ido; Kinzel, Wolfgang

doi:10.1103/physrevlett.91.118701

Cited by 81 publications

(26 citation statements)

References 10 publications

(19 reference statements)

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“…Next to algorithmic extensions to further increase the security [21,22,15,23], architectural improvements or variants include a fully serial realisation with TDMA usage of a single TPM unit. This further decreases the area consumption but at the cost of an increase in necessary cycles for one output bit.…”

Section: Discussionmentioning

confidence: 99%

Tree Parity Machine Rekeying Architectures

Volkmer

Wallner

2005

IEEE Trans. Comput.

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The necessity to secure the communication between hardware components in embedded systems becomes increasingly important with regard to the secrecy of data and particularly its commercial use. We suggest a low-cost (i.e. small logic-area) solution for flexible security levels and short key lifetimes. The basis is an approach for symmetric key exchange using the synchronisation of Tree Parity Machines. Fast successive key generation enables a key exchange within a few milliseconds, given realistic communication channels with a limited bandwidth. For demonstration we evaluate characteristics of a standard-cell ASIC design realisation as IP-core in 0.18µ-technology.

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Section: Discussionmentioning

confidence: 99%

Tree Parity Machine Rekeying Architectures

Volkmer

Wallner

2005

IEEE Trans. Comput.

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“…Since the first proposal [1] of the neural key-exchange protocol, improved strategies for the attackers [11,12] and the partners [5,13,14] have been suggested and analyzed [2,15,16,17]. For the geometric attack it has been found that the synaptic depth L determines the security of the system: the success probability P E decreases exponentially with L, while the synchronization time t sync increases only proportionally to L 2 [17,18].…”

Section: Introductionmentioning

confidence: 99%

Genetic attack on neural cryptography

Ruttor¹,

Kinzel²,

Naeh³

et al. 2006

Phys. Rev. E

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Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

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“…However, Kinzel [9,10,11] does not deal with the teacher-student relation since a teacher is not employed in his model. In contrast to Kinzel's model, we have proposed mutual learning between two students who learn from a teacher in advance [12].…”

Section: Introductionmentioning

confidence: 99%

“…As a model involving the interaction between students, Kinzel proposed mutual learning within the framework of on-line learning [9,10,11]. Kinzel's model employs two students, and a student learns with the other student acting as a teacher.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

Hara

Yamada

2008

J. Phys. Soc. Jpn.

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Abstruct-We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The proposed model consists of two learning steps: two students independently learn from a teacher, and then the students learn from each other through the mutual learning. In mutual learning, students learn from each other and the generalization error is improved even if the teacher has not taken part in the mutual learning. However, in the case of different initial overlaps(direction cosine) between teacher and students, a student with a larger initial overlap tends to have a larger generalization error than that of before the mutual learning. To overcome this problem, our proposed optimization method of mutual learning optimizes the step sizes of two students to minimize the asymptotic property of the generalization error. Consequently, the optimized mutual learning converges to a generalization error identical to that of the optimal ensemble learning. In addition, we show the relationship between the optimum step size of the mutual learning and the integration mechanism of the ensemble learning.

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Public Channel Cryptography by Synchronization of Neural Networks and Chaotic Maps

Cited by 81 publications

References 10 publications

Tree Parity Machine Rekeying Architectures

Tree Parity Machine Rekeying Architectures

Genetic attack on neural cryptography

Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning

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