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We propose graph-based predictable feature analysis (GPFA), a new method for unsupervised learning of predictable features from high-dimensional time series, where high predictability is understood very generically as low variance in the distribution of the next data point given the previous ones. We show how this measure of predictability can be understood in terms of graph embedding as well as how...
For the last ten years, side channel research has focused on extracting data leakage with the goal of recovering secret keys of embedded cryptographic implementations. For about the same time it has been known that side channel leakage contains information about many other internal processes of a computing device. In this work we exploit side channel information to recover large parts of the...
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