Abstract
The advent of CUDA-enabled GPU makes it possible to provide cloud applications with high-performance data security services. Unfortunately, recent studies have shown that GPU-based applications are also susceptible to side-channel attacks. These published work studied the side-channel vulnerabilities of GPU-based AES implementations by taking the advantage of the cache sharing among multiple threads or high parallelism of GPUs. Therefore, for GPU-based bitsliced cryptographic implementations, which are immune to the cache-based attacks referred to above, only a power analysis method based on the high-parallelism of GPUs may be effective. However, the leakage model used in the power analysis is not efficient at all in practice. In light of this, we investigate electro-magnetic (EM) side-channel vulnerabilities of a GPU-based bitsliced AES implementation from the perspective of bit-level parallelism and thread-level parallelism in order to make the best of the localization effect of EM leakage with parallelism. Specifically, we propose efficient multi-bit and multi-thread combinational analysis techniques based on the intrinsic properties of bitsliced ciphers and the effect of multi-thread parallelism of GPUs, respectively. The experimental result shows that the proposed combinational analysis methods perform better than non-combinational and intuitive ones. Our research suggests that multi-thread leakages can be used to improve attacks if the multi-thread leakages are not synchronous in the time domain.
Highlights
Nowadays as the most widely used parallel computing platform, Graphics Processing Unit (GPU) has evolved from a special hardware for graphics rendering into a general-purpose computing device for various applications as biomedical analysis, signal processing, scientific computing and so on
We study more efficient side-channel attacks against a GPU-based bitsliced AES implementation in order to give a deeper insight into the side-channel vulnerabilities of GPU-based cryptographic implementations in the perspective of parallelism
More details about the multi-bit decision combinational analysis (MB-DCA) is showed in Experimental results and discussion Since the number of multi-slice groups and multi-thread groups for the GPU-based bitsliced AES encryption of (32 × 32 × 16)-byte plaintext within a GPU warp have nothing to do with multibit feature combinational analysis (MB-FCA) and MB-DCA, our experiments are performed in a simplified mode, say T1G-S1G encryption mode
Summary
Nowadays as the most widely used parallel computing platform, Graphics Processing Unit (GPU) has evolved from a special hardware for graphics rendering into a general-purpose computing device for various applications as biomedical analysis, signal processing, scientific computing and so on. GPU-based applications are vulnerable to many known attacks as proposed in Di et al (2016); Naghibijouybari et al (2018); Jiang et al (2016). Among those published vulnerabilities (2020) 3:3 cryptosystems that are resistant to cache-timing attacks (Jiang et al 2016; 2017) and cache-based EM attacks (Gao et al 2018; Gao et al 2018). We study more efficient side-channel attacks against a GPU-based bitsliced AES implementation in order to give a deeper insight into the side-channel vulnerabilities of GPU-based cryptographic implementations in the perspective of parallelism
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