Description
Deep neural networks (DNNs) undergo lengthy and expensive training procedures whose outcome - the DNN weights - represents a significant intellectual property asset to protect.
Side-channel analysis (SCA) has recently appeared as an effective approach to recover this confidential asset of DNN implementations.
Ding et al. (HOST’25) introduced MACPRUNING, a novel SCA countermeasure based on pruning, a performance-oriented Approximate Computing technique: at inference time, the implementation randomly prunes (or skips) non-important weights (i.e., with low contribution to the DNN’s accuracy) of the first layer, exponentially increasing the side-channel resilience of the protected DNN implementation.
In this presentation, we describe a preprocessing methodology taking advantage of a control-flow dependency intrinsic to the countermeasure's design.
Through practical experiments, we demonstrate the effectiveness of our methodology, recovering up to 96% of the important weights of a MACPruning-protected Multi-Layer Perceptron.
Moreover, we show how microarchitectural leakage improves the effectiveness of our methodology, even allowing for the recovery of up to 100% of the targeted non-important weights.
Lastly, by adapting our methodology, we elaborate on how the pruning mechanism, which depends on the importance of the weights, enables the circumvention of a control-flow-free MACPRUNING implementation.
With this last point, we identify the pruning mechanism underlying MACPRUNING as the root of the countermeasure’s vulnerability.
Autre
Présentation en Anglais (slides en Anglais)
Talk in English (slides in English)
Practical infos
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