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 s...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.展开更多
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 s...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.展开更多
基金This work was supported in part by National Natural Science Foundation of China(No.61632020,UI936209)Beijing National Science Foundation(No.4192067).
文摘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.
基金supported in part by National Natural Science Foundation of China(No.61632020,UI936209)Beijing National Science Foundation(No.4192067).
文摘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.
