Side Channel Attacks on Resource-Constrained Devices Enabled Through Secure Cloud Outsourcing

Side-Channel Attacks (SCAs) now require more side-channel traces for successful execution, which places more stringent requirements on the storage capacity and computational ability of the devices on which SCAs are based. To reduce the storage and computational pressure on the local device where SCAs are performed on collected leakage traces from an attacking device, this paper proposes a secure cloud outsourcing protocol to perform Principal Component Analysis (PCA) dimensionality reduction on the side-channel traces. Secure cloud outsourcing is applied for the computationally intensive matrix multiplication and eigenvalue decomposition of the PCA process. The proposed protocol has been proven to balance privacy, efficiency, and correctness. Through experiments on CW and Grizzly datasets, it shows that 1) Correlation Power Analysis (CPA) with PCA effectively mitigates noise, improving the probability of a successful CPA; 2) Cloud-based PCA significantly reduces the computational complexity of local devices; 3) Template Attacks (TAs) are performed on leakage trace data using cloud-based PCA, client-based PCA, Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). The attack results of cloud-based and client-based PCA are basically identical, and they achieve lower Guessing-Entropy (GE) than ICA. Both theoretical analysis and experimental results demonstrate the feasibility and advantages of this protocol.