Efficient Side-Channel Data Compression Using Autoencoder Networks for Enhanced Leakage Analysis

Side-channel data compression techniques are designed to reduce the dimensionality of input data while preserving critical information, thereby improving the efficiency of side-channel analysis. Traditional methods of side-channel analysis can be broadly classified into two categories: peak extraction techniques and data integration techniques, based on the scale and principles of the input data. In this study, we present a novel side-channel data compression technique utilizing convolutional autoencoder networks. We apply this method to power-side channel data collected from an SM4 encryption circuit implemented on a secure chip. Experimental results show that our proposed technique surpasses conventional methods in Test Vector Leakage Assessment (TVLA) analysis and achieves a 35% improvement in efficiency in Measurements to Disclosure (MTD) analysis, while maintaining the same compression rate. These results underscore the effectiveness and potential of our approach in enhancing side-channel analysis.