GE vs GM: Efficient side-channel security evaluations on full cryptographic keys

Security evaluations for full cryptographic keys is a very important research topic since the past decade. An efficient rank estimation algorithm was proposed at FSE 2015 to approximate the empirical guessing entropy remaining after a side-channel attack on a full AES key, by combining information from attacks on each byte of he key independently. However, these could not easily scale to very large keys over 1024 bits. Hence, at CHES 2017, it was proposed a new approach for scalable security evaluations based on Massey’s guessing entropy, which was shown tight and scalable to very large keys, even beyond 8192 bits. Then, at CHES 2020, it was proposed a new method for estimating the empirical guessing entropy for the case of full-key evaluations, showing also important divergences between the empirical guessing entropy and Massey’s guessing entropy. However, there has been some confusion in recent publications of side-channel evaluation methods relying on these two variants of the guessing entropy. Furthermore, it remained an open problem to decide which of these methods should be used and in which context, particularly given the wide acceptance of the empirical guessing entropy in the side-channel community and the relatively little use of the other. In this paper, we tackle this open problem through several contributions. First of all, we provide an unitary presentation of both versions of the guessing entropy, allowing an easy comparison of the two metrics. Secondly, we compare the two metrics using a set of common and relevant indicators, as well as three different datasets for side-channel evaluations (simulated, AVR XMEGA 8-bit microcontroller and a 32-bit device). We used these indicators and datasets also to compare the three full-key evaluation methods from FSE 2015, CHES 2017 and CHES 2020, allowing us to provide a clear overview of the usefulness and limitations of each method. Furthermore, our analysis has enabled us to find a new method for verifying the soundness of a leakage model, by comparing both versions of the guessing entropy. This method can be easily extended to full-key evaluations, hence leading to a new useful method for side-channel evaluations

Side-Channel Attacks on Masked Bitsliced Implementations of AES

In this paper, we provide a detailed analysis of CPA and Template Attacks on masked implementations of bitsliced AES, targeting a 32-bit platform through the ChipWhisperer side-channel acquisition tool. Our results show that Template Attacks can recover the full AES key successfully within 300 attack traces even on the masked implementation when using a first-order attack (no pre-processing). Furthermore, we confirm that the SubBytes operation is overall a better target for Template Attacks due to its non-linearity, even in the case of bitsliced implementations, where we can only use two bits per key byte target. However, we also show that targeting the AddRoundKey can be used to attack bitsliced implementations and that, in some cases, it can be more efficient than the SubBytes attack

Side-Channel Attacks on Masked Bitsliced Implementations of AES

In this paper, we provide a detailed analysis of CPA and Template Attacks on masked implementations of bitsliced AES, targeting a 32-bit platform through the ChipWhisperer side-channel acquisition tool. Our results show that Template Attacks can recover the full AES key successfully within 300 attack traces even on the masked implementation when using a first-order attack (no pre-processing). Furthermore, we confirm that the SubBytes operation is overall a better target for Template Attacks due to its non-linearity, even in the case of bitsliced implementations, where we can only use two bits per key byte target. However, we also show that targeting the AddRoundKey can be used to attack bitsliced implementations and that, in some cases, it can be more efficient than the SubBytes attack

GE vs GM: Efficient side-channel security evaluations on full cryptographic keys

Security evaluations for full cryptographic keys is a very important research topic since the past decade. An efficient rank estimation algorithm was proposed at FSE 2015 to approximate the empirical guessing entropy remaining after a side-channel attack on a full AES key, by combining information from attacks on each byte of he key independently. However, these could not easily scale to very large keys over 1024 bits. Hence, at CHES 2017, it was proposed a new approach for scalable security evaluations based on Massey’s guessing entropy, which was shown tight and scalable to very large keys, even beyond 8192 bits. Then, at CHES 2020, it was proposed a new method for estimating the empirical guessing entropy for the case of full-key evaluations, showing also important divergences between the empirical guessing entropy and Massey’s guessing entropy. However, there has been some confusion in recent publications of side-channel evaluation methods relying on these two variants of the guessing entropy. Furthermore, it remained an open problem to decide which of these methods should be used and in which context, particularly given the wide acceptance of the empirical guessing entropy in the side-channel community and the relatively little use of the other.In this paper, we tackle this open problem through several contributions. First of all, we provide an unitary presentation of both versions of the guessing entropy, allowing an easy comparison of the two metrics. Secondly, we compare the two metrics using a set of common and relevant indicators, as well as three different datasets for side-channel evaluations (simulated, AVR XMEGA 8-bit microcontroller and a 32-bit device). We used these indicators and datasets also to compare the three full-key evaluation methods from FSE 2015, CHES 2017 and CHES 2020, allowing us to provide a clear overview of the usefulness and limitations of each method. Furthermore, our analysis has enabled us to find a new method for verifying the soundness of a leakage model, by comparing both versions of the guessing entropy. This method can be easily extended to full-key evaluations, hence leading to a new useful method for side-channel evaluations

Score-Based vs. Probability-Based Enumeration - A Cautionary Note

The fair evaluation of leaking devices generally requires to come with the best possible distinguishers to extract and exploit side-channel information. While the need of a sound model for the leakages is a well known issue, the risks of additional errors in the post-processing of the attack results (with key enumeration/key rank estimation) are less investigated. Namely, optimal post-processing is known to be possible with distinguishers outputting probabilities (e.g. template attacks), but the impact of a deviation from this context has not been quantified so far. We therefore provide a consolidating experimental analysis in this direction, based on simulated and actual measurements. Our main conclusions are twofold. We first show that the concrete impact of heuristic scores such as produced with a correlation power analysis can lead to non-negligible post-processing errors. We then show that such errors can be mitigated in practice, with Bayesian extensions or specialized distinguishers (e.g. on-the-fly linear regression)