Presents optimization algorithms for Shannon mitigation of timing side channels in a functional-observation threat model using entropy objectives.
arXiv preprint arXiv:1808.10502 (2018)
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Neural networks are trained as timing models of programs and analyzed via MILP to detect and quantify timing side-channel information leaks.
citing papers explorer
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Quantitative Mitigation of Timing Side Channels
Presents optimization algorithms for Shannon mitigation of timing side channels in a functional-observation threat model using entropy objectives.
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Efficient Detection and Quantification of Timing Leaks with Neural Networks
Neural networks are trained as timing models of programs and analyzed via MILP to detect and quantify timing side-channel information leaks.