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Channel Capacity of Concurrent Probabilistic Programs

Department of Computer Science, University of Tabriz, Tabriz 51666-16471, Iran
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Entropy 2019, 21(9), 885; https://doi.org/10.3390/e21090885
Received: 6 July 2019 / Revised: 31 July 2019 / Accepted: 20 August 2019 / Published: 12 September 2019
Programs are under continuous attack for disclosing secret information, and defending against these attacks is becoming increasingly vital. An attractive approach for protection is to measure the amount of secret information that might leak to attackers. A fundamental issue in computing information leakage is that given a program and attackers with various knowledge of the secret information, what is the maximum amount of leakage of the program? This is called channel capacity. In this paper, two notions of capacity are defined for concurrent probabilistic programs using information theory. These definitions consider intermediate leakage and the scheduler effect. These capacities are computed by a constrained nonlinear optimization problem. Therefore, an evolutionary algorithm is proposed to compute the capacities. Single preference voting and dining cryptographers protocols are analyzed as case studies to show how the proposed approach can automatically compute the capacities. The results demonstrate that there are attackers who can learn the whole secret of both the single preference protocol and dining cryptographers protocol. The proposed evolutionary algorithm is a general approach for computing any type of capacity in any kind of program. View Full-Text
Keywords: channel capacity; information theory; evolutionary algorithms; quantitative information flow; concurrent probabilistic programs channel capacity; information theory; evolutionary algorithms; quantitative information flow; concurrent probabilistic programs
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Salehi, K.; Karimpour, J.; Izadkhah, H.; Isazadeh, A. Channel Capacity of Concurrent Probabilistic Programs. Entropy 2019, 21, 885.

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