Abstract
Differential Fault Analysis (DFA) is one of the most practical methods to recover the secret keys from real cryptographic devices. In particular, DFA on Advanced Encryption Standard (AES) has been massively researched for many years for both single-byte and multibyte fault model. For AES, the first proposed DFA attack requires 6 pairs of ciphertexts to identify the secret key under multibyte fault model. Until now, the most efficient DFA under multibyte fault model proposed in 2017 can complete most of the attacks within 3 pairs of ciphertexts. However, we note that the attack is not fully optimized since no clear optimization goal was set. In this work, we introduce two optimization goals as the fewest ciphertext pairs and the least computational complexity. For these goals, we manage to figure out the corresponding optimized key recovery strategies, which further increase the efficiency of DFA attacks on AES. A more accurate security assessment of AES can be completed based on our study of DFA attacks on AES. Considering the variations of fault distribution, the improvement to the attack has been analyzed and verified.
Highlights
In the age of IoT, IoT technologies can widely perceive our physical world and generate sensing data for further research
6 pairs of ciphertexts are required in Differential Fault Analysis (DFA) attacks under multibyte fault model
In our method using datacomplexity priority strategy, most of the DFA attacks can be completed within 5 pairs of ciphertexts
Summary
In the age of IoT, IoT technologies can widely perceive our physical world and generate sensing data for further research. There are lots of scenarios in IoT where people have to collaborate through devices to complete tasks; for example, a device sends data to other devices [1], or one user shares EHR in mobile cloud computing [2], and these transmitted data are often the privacy data of users. In the big data environment [3, 4], many enterprises need to constantly assimilate big data knowledge and private knowledge by multiple knowledge transfers to maintain their competitive advantage [5]. Attackers develop fault attacks [6] on cryptographic devices and the private information is leaked. A lot of sensitive data suffer from severe security and privacy threats
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