Poly-Logarithmic Side Channel Rank Estimation via Exponential Sampling

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose ESrank, the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity, which also achieves excellent practical performance. Our main idea is to use exponential sampling to drastically reduce the algorithm\u27s complexity. Importantly, ESrank is simple to build from scratch, and requires no algorithmic tools beyond a sorting function. After rigorously bounding the accuracy, time and space complexities, we evaluated the performance of ESrank on a real SCA data corpus, and compared it to the currently-best histogram-based algorithm. We show that ESrank gives excellent rank estimation (with roughly a 1-bit margin between lower and upper bounds), with a performance that is on-par with the Histogram algorithm: a run-time of under 1 second on a standard laptop using 6.5 MB RAM

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

Tighter, faster, simpler side-channel security evaluations beyond computing power

A Eurocrypt 2013 paper Security evaluations beyond computing power: How to analyze side-channel attacks you cannot mount? by Veyrat-Charvillon, Gérard, and Standaert proposed a Rank Estimation Algorithm (REA) to estimate the difficulty of finding a secret key given side-channel information from independent subkeys, such as the 16 key bytes in AES-128 or the 32 key bytes in AES-256. The lower and upper bounds produced by the algorithm are far apart for most key ranks. The algorithm can produce tighter bounds but then becomes exponentially slower; it also becomes exponentially slower as the number of subkeys increases. This paper introduces two better algorithms for the same problem. The first, the Extended Rank Estimation Algorithm (EREA), is an extension of REA using statistical sampling as a second step to increase the speed of tightening the bounds on the rank. The second, the Polynomial Rank Outlining Algorithm (PRO), is a new approach to computing the rank. PRO can handle a much larger number of subkeys efficiently, is easy to implement in a computer-algebra system such as Sage, and produces much tighter bounds than REA in less time

PRank: Fast Analytical Rank Estimation via Pareto Distributions

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose a new method called PRank for rank estimation, that is conceptually simple, and more time and memory efficient than previous proposals. Our main idea is to bound each subkey distribution by a Pareto-like function: since these are analytical functions, we can then estimate the rank by a closed formula. We evaluated the performance of PRank through extensive simulations based on two real SCA data corpora, and compared it to the currently-best histogram-based algorithm. We show that PRank gives a good rank estimation with much improved time and memory efficiency, especially for large ranks: For ranks between $2^{80}-2^{100}$ PRank estimation is at most 10 bits above the histogram rank and for ranks beyond $2^{100}$ the PRank estimation is only 4 bits above the histogram rank---yet it runs faster, and uses negligible memory. PRank gives a new and interesting method to solve the rank estimation problem based on reduction to analytical functions and calculating one closed formula hence using negligible time and space

Simpler and More Efficient Rank Estimation for Side-Channel Security Assessment

Rank estimation algorithms allow analyzing the computational security of cryptographic keys for which adversaries have obtained partial information thanks to leakage or cryptanalysis. They are particularly useful in side-channel security evaluations, where the key is known by the evaluator but not reachable with exhaustive search. A first instance of such algorithms has been proposed at Eurocrypt 2013. In this paper, we propose a new tool for rank estimation that is conceptually simpler and much more efficient than this previous proposal. It allows approximating the key rank of (128-bit, 256-bit) symmetric keys with very tight bounds (i.e. with less than one bit of error), almost instantaneously and with limited memory. It also scales nicely to larger (e.g. 1024-bit) key sizes, for which the previous algorithm was hardly applicable

Making Masking Security Proofs Concrete - Or How to Evaluate the Security of any Leaking Device

We investigate the relationships between theoretical studies of leaking cryptographic devices and concrete security evaluations with standard side-channel attacks. Our contributions are in four parts. First, we connect the formal analysis of the masking countermeasure proposed by Duc et al. (Eurocrypt 2014) with the Eurocrypt 2009 evaluation framework for side-channel key recovery attacks. In particular, we re-state their main proof for the masking countermeasure based on a mutual information metric, which is frequently used in concrete physical security evaluations. Second, we discuss the tightness of the Eurocrypt 2014 bounds based on experimental case studies. This allows us to conjecture a simplified link between the mutual information metric and the success rate of a side-channel adversary, ignoring technical parameters and proof artifacts. Third, we introduce heuristic (yet well-motivated) tools for the evaluation of the masking countermeasure when its independent leakage assumption is not perfectly fulfilled, as it is frequently encountered in practice. Thanks to these tools, we argue that masking with non-independent leakages may provide improved security levels in certain scenarios. Eventually, we consider the tradeoff between measurement complexity and key enumeration in divide-and-conquer side-channel attacks, and show that it can be predicted based on the mutual information metric, by solving a non-linear integer programming problem for which efficient solutions exist. The combination of these observations enables significant reductions of the evaluation costs for certification bodies