An Instruction Set Extension to Support Software-Based Masking 📺
In both hardware and software, masking can represent an effective means of hardening an implementation against side-channel attack vectors such as Differential Power Analysis (DPA). Focusing on software, however, the use of masking can present various challenges: specifically, it often 1) requires significant effort to translate any theoretical security properties into practice, and, even then, 2) imposes a significant overhead in terms of efficiency. To address both challenges, this paper explores the use of an Instruction Set Extension (ISE) to support masking in software-based implementations of a range of (symmetric) cryptographic kernels including AES: we design, implement, and evaluate such an ISE, using RISC-V as the base ISA. Our ISE-supported first-order masked implementation of AES, for example, is an order of magnitude more efficient than a software-only alternative with respect to both execution latency and memory footprint; this renders it comparable to an unmasked implementation using the same metrics, but also first-order secure.
FENL: an ISE to mitigate analogue micro-architectural leakage 📺
Ge et al. [GYH18] propose the augmented ISA (or aISA), a central tenet of which is the selective exposure of micro-architectural resources via a less opaque abstraction than normal. The aISA proposal is motivated by the need for control over such resources, for example to implement robust countermeasures against microarchitectural attacks. In this paper, we apply an aISA-style approach to challenges stemming from analogue micro-architectural leakage; examples include power-based Hamming weight and distance leakage from relatively fine-grained resources (e.g., pipeline registers), which are not exposed in, and so cannot be reliably controlled via, a normal ISA. Specifically, we design, implement, and evaluate an ISE named FENL: the ISE acts as a fence for leakage, preventing interaction between, and hence leakage from, instructions before and after it in program order. We demonstrate that the implementation and use of FENL has relatively low overhead, and represents an effective tool for systematically localising and reducing leakage.
Share-slicing: Friend or Foe? 📺
Masking is a well loved and widely deployed countermeasure against side channel attacks, in particular in software. Under certain assumptions (w.r.t. independence and noise level), masking provably prevents attacks up to a certain security order and leads to a predictable increase in the number of required leakages for successful attacks beyond this order. The noise level in typical processors where software masking is used may not be very high, thus low masking orders are not sufficient for real world security. Higher order masking however comes at a great cost, and therefore a number techniques have been published over the years that make such implementations more efficient via parallelisation in the form of bit or share slicing. We take two highly regarded schemes (ISW and Barthe et al.), and some corresponding open source implementations that make use of share slicing, and discuss their true security on an ARM Cortex-M0 and an ARM Cortex-M3 processor (both from the LPC series). We show that micro-architectural features of the M0 and M3 undermine the independence assumptions made in masking proofs and thus their theoretical guarantees do not translate into practice (even worse it seems unpredictable at which order leaks can be expected). Our results demonstrate how difficult it is to link theoretical security proofs to practical real-world security guarantees.
Direct Construction of Optimal Rotational-XOR Diffusion Primitives
As a core component of SPN block cipher and hash function, diffusion layer is mainly introduced by matrices built from maximum distance separable (MDS) codes. Up to now, most MDS constructions require to perform an equivalent or even exhaustive search. In this paper, we study the cyclic structure of rotational-XOR diffusion layer, a commonly used diffusion primitive over (