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Abstract: Since their publication in 1998 and 2001, respectively, Power and Electromagnetic Analysis (SPA, DPA, EMA) have been successfully used to retrieve secret information stored in cryptographic devices. Both attacks usually model the side-channel leakages using the so-called “Hamming weight” and “Hamming distance” models, i.e. they only consider the number of bit transitions in a device as an image of its leakage. In these models, the main difference between power and electromagnetic analysis is assumed to be the fact that the latter allows space localization (i.e. to observe the leakage of only a part of the cryptographic device). In this paper, we make use of a more accurate leakage model for CMOS devices and investigate its consequences. In particular, we show that it is practically feasible to distinguish between and bit transitions in certain implementations and that electromagnetic analysis is particularly efficient in this respect. We denote this model as the “switching distance” leakage model and show how it may be very helpful to defeat some commonly used countermeasures (e.g. data buses precharged with random values). Then, we compare the different models and stress their respective constraints/advantages regarding practical attacks. [Copyright &y& Elsevier]

Summary: Noise is an important ingredient for side‐channel‐analysis countermeasures security. However, physical noise is in most cases not sufficient to achieve high‐security levels. As an outcome, designers traditionally aim to emulate noise by harnessing shuffling in the time domain and algorithmic noise in the amplitude domain. On one hand, harnessing algorithmic noise is limited in architectures/devices which have a limited data‐path width. On the other hand, the performance degradation due to shuffling is considerable. A natural complement to operation shuffling is the hardware‐based intra‐cycle shuffling (ICS), which typically shuffles the sample time of bits within a clock cycle (instead of micro‐processor operations). Such architecture eliminates the performance overhead due to shuffling within a single cycle, it is algorithm‐independent, i.e., no need in partitioning of operations, and as it is hardware‐based, the data‐path width can be tailored to better exploit algorithmic‐noise. In this manuscript, we first analyze the noise components in physical designs to better model the algorithmic noise. We then perform an information‐theoretic (IT) analysis of both shuffling countermeasures. The last part of the manuscript deals with real‐world architectures analysis: IT analysis of an Advanced Encryption Standard (AES) core implemented over a 32‐ and 128‐bit wide data‐path embedded with intra‐cycle shuffling and two flavors of shuffling generation (memory‐based and on‐line permutation generation). The manuscript is concluded by underling the benefits which can be achieved with the ICS architecture. [ABSTRACT FROM AUTHOR]

Algorithmic decision making is used in an increasing number of fields. Letting automated processes take decisions raises the question of their accountability. In the field of computational journalism, the algorithmic accountability framework proposed by Diakopoulos formalizes this challenge by considering algorithms as objects of human creation, with the goal of revealing the intent embedded into their implementation. A consequence of this definition is that ensuring accountability essentially boils down to a transparency question: given the appropriate reverse-engineering tools, it should be feasible to extract design criteria and to identify intentional biases. General limitations of this transparency ideal have been discussed by Ananny and Crawford (New Media Soc 20(3):973–989, 2018). We further focus on its technical limitations. We show that even if reverse-engineering concludes that the criteria embedded into an algorithm correspond to its publicized intent, it may be that adversarial behaviors make the algorithm deviate from its expected operation. We illustrate this issue with an automated news recommendation system, and show how the classification algorithms used in such systems can be fooled with hard-to-notice modifications of the articles to classify. We therefore suggest that robustness against adversarial behaviors should be taken into account in the definition of algorithmic accountability, to better capture the risks inherent to algorithmic decision making. We finally discuss the various challenges that this new technical limitation raises for journalism practice. [ABSTRACT FROM AUTHOR]

Noise in digital circuits has always been minimized to achieve high signal integrity, robust operation and of course high performance. However, for cryptographic applications, increased noise can in fact be beneficial. It can be used effectively to reduce the (cryptographic) Signal-to-Noise (SNR) ratio and to make it harder for an adversary to extract useful information (e.g., secret keys) from the side channel leakage data. A natural question concerns the extent to which intrinsic (internal) noise is required to improve security. In this manuscript, we explore this question and further introduce a methodology to exploit the intrinsic physical noise (i.e., flicker- and thermal-noise) at the secure circuit level. We additionally demonstrate how the values obtained from our methodology translate into relevant cryptographic metrics. Our simulations show that the calculated cryptographic noise values are in close agreement with the noise levels extracted from noisy distributions using transient noise analysis. We finally evaluate (with the proposed methodology) several meaningful parameters which affect the internal noise (and their security extent) such as transistors-sizing and voltage-supply changes. [ABSTRACT FROM AUTHOR]