Abstract Recent advances in machine learning have enabled Neural Network (NN) inference directly on constrained embedded devices. This local approach enhances the privacy of user data, as the inputs to the NN inference are not shared with third-party cloud providers over a communication network. At the same time, however, performing local NN inference on embedded devices opens up the possibility of Power Analysis attacks, which have recently been shown to be effective in recovering NN parameters, as well as their activations and structure. Knowledge of these NN characteristics constitutes a privacy threat, as it enables highly effective Membership Inference and Model Inversion attacks, which can recover information about the sensitive data that the NN model was trained on. In this paper we address the problem of securing sensitive NN inference parameters against Power Analysis attacks. Our approach employs masking, a countermeasure well-studied in the context of cryptographic algorithms. We design a set of gadgets, i.e., masked operations, tailored to NN inference. We prove our proposed gadgets secure against power attacks and show, both formally and experimentally, that they are composable, resulting in secure NN inference. We further propose optimizations that exploit intrinsic characteristics of NN inference to reduce the masking’s runtime and randomness requirements. We empirically evaluate the performance of our constructions, showing them to incur a slowdown by a factor of about 2–5.

As the new hash standard, Keccak-based secure hash function (SHA-3) will be used in various cryptographic applications. Its security will be of paramount importance to the systems built on top of it. This paper proposes efficient algebraic fault analysis (AFA) methods, and for the first time, applies them to all four modes of SHA-3 under relaxed fault models. Our AFA utilizes the clear algebraic properties of Keccak operations and is very suitable for the fault analysis of SHA-3. Both our analysis and experimental results show that the proposed AFA method is more efficient than the traditional differential fault analysis (DFA) under the single-byte fault model, requiring much fewer faults to recover a whole internal state of the hashing computation. Meanwhile, as AFA is able to exploit all the information available, it can be applied to SHA-3 modes with shorter digests and under more relaxed fault models, where often times the DFA method fails. Our results show that AFA can successfully break all the four SHA-3 modes under a 16-bit fault model, and break SHA3-512 under an even more relaxed fault model, 32-bit fault, all within several minutes. The successful AFA on SHA-3 demonstrates the vulnerability of Keccak algorithms to fault analysis, calling for protections against fault injection and fault analysis.

This paper presents an efficient algebraic fault analysis on all four modes of SHA-3 under relaxed fault models. This is the first work to apply algebraic techniques on fault analysis of SHA-3. Results show that algebraic fault analysis on SHA-3 is very efficient and effective due to the clear algebraic properties of Keccak operations. Comparing with previous work on differential fault analysis of SHA-3, algebraic fault analysis can identify the injected faults with much higher rates, and recover an entire internal state of the penultimate round with much fewer fault injections.

Side-channel attack utilizes side-channel leakages to extract the secret in crypto systems. Various countermeasures for different algorithms and platforms have been proposed to protect crypto systems against such attacks. Manual countermeasure design requires deep understanding of the target algorithm and implementation, and oftentimes is platform-specific and error-prone. In this paper, we propose the construction of Threshold Implementation (TI), a provably secure countermeasure against power attacks, as an automated compiler pass in the open LLVM (Low Level Virtual Machine) framework. Attack results show that the automatically generated TI designs are secure against power attacks. As our proposed scheme implements the countermeasure at the intermediate representation (IR) level, our method can be applied to any cipher software in any programming language, and the generated implementations can be ported to different platforms and architectures.