Secure Data Deduplication with Resistance to Side-Channel Attacks via Fog Computing

Author(s):  
Fuyou Zhang ◽  
Saiyu Qi ◽  
Haoran Yuan ◽  
Meng Zhang
2021 ◽  
pp. 1-1
Author(s):  
Youshui Lu ◽  
Yong Qi ◽  
Saiyu Qi ◽  
Fuyou Zhang ◽  
Wei Wei ◽  
...  

2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.


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