Zahra Eskandari; Abbas Ghaemi Bafghi
Abstract
Cube Attack is a successful case of Algebraic Attack. Cube Attack consists of two phases, linear equation extraction and solving the extracted equation system. Due to the high complexity of equation extraction phase in finding linear equations, we can extract nonlinear ones that could be approximated ...
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Cube Attack is a successful case of Algebraic Attack. Cube Attack consists of two phases, linear equation extraction and solving the extracted equation system. Due to the high complexity of equation extraction phase in finding linear equations, we can extract nonlinear ones that could be approximated to linear equations with high probability. The probabilistic equations could be considered as linear ones under some noises. Existing approaches to solve noisy equation systems work well provided that the equation system has low error rate; however, as the error rate increases, the success rate of finding the exact solution diminishes, making them rather inefficient in high error rate. In this paper, we extend Cube Attack to probabilistic equations. First, an approximation approach based on linear combinations of nonlinear equations is presented to find probabilistic linear equations with high probability. Then, we present an approach to improve the efficiency of current solving approaches and make them practical to solve high error rate linear equation system. Finally, utilizing proposed approaches, we find the right key under extended noisy equation system with lower complexity in comparison to the original Cube Attack.
H. Shakeri; A. Ghaemi Bafghi
Abstract
It is a common and useful task in a web of trust to evaluate the trust value between two nodes using intermediate nodes. This technique is widely used when the source node has no experience of direct interaction with the target node, or the direct trust is not reliable enough by itself. If trust is used ...
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It is a common and useful task in a web of trust to evaluate the trust value between two nodes using intermediate nodes. This technique is widely used when the source node has no experience of direct interaction with the target node, or the direct trust is not reliable enough by itself. If trust is used to support decision-making, it is important to have not only an accurate estimate of trust, but also a measure of confidence in the intermediate nodes as well as the final estimated value of trust. The present paper thus aims to introduce a novel framework for integrated representation of trust and confidence using intervals, which provides two operations of trust interval multiplication and summation. The former is used for computing propagated trust and confidence, whereas the latter provides a formula for aggregating different trust opinions. The properties of the two operations are investigated in details. This study also proposes a time-variant method that considers freshness, expertise level and two similarity measures in confidence estimation. The results indicate that this method is more accurate compared to the existing ones. In this regard, the results of experiments carried out on two well-known trust datasets are reported and analyzed, showing that the proposed method increases the accuracy of trust inference in comparison with the existing methods.