Combining High-Level and Low-Level Approaches to Evaluate Software Implementations Robustness Against Multiple Fault Injection Attacks

International audiencePhysical fault injections break security functionalities of algorithms by targeting their implementations. Software techniques strengthen such implementations to enhance their robustness against fault attacks. Exhaustively testing physical fault injections is time consuming and requires complex platforms. Simulation solutions are developed for this specific purpose. We chose two independent tools presented in 2014, the Laser Attack Robustness (Lazart) and the Embedded Fault Simulator (EFS) in order to evaluate software implementations against multiple fault injection attacks. Lazart and the EFS share the common goal that consists in detecting vulnerabilities in the code. However, they operate with different techniques , fault models and abstraction levels. This paper aims at exhibiting specific advantages of both approaches and proposes a combining scheme that emphasizes their complementary nature

Improved Zero-Knowledge Argument of Encrypted Extended Permutation

Extended permutation (EP) is a generalized notion of the standard permutation. Unlike the one-to-one correspondence mapping of the standard permutation, EP allows to replicate or omit elements as many times as needed during the mapping. EP is useful in the area of secure multi-party computation (MPC), especially for the problem of private function evaluation (PFE). As a special class of MPC problems, PFE focuses on the scenario where a party holds a private circuit $C$ while all other parties hold their private inputs $x_1, \ldots, x_n$, respectively. The goal of PFE protocols is to securely compute the evaluation result $C(x_1, \ldots, x_n)$, while any other information beyond $C(x_1, \ldots, x_n)$ is hidden. EP here is introduced to describe the topological structure of the circuit $C$, and it is further used to support the evaluation of $C$ privately. For an actively secure PFE protocol, it is crucial to guarantee that the private circuit provider cannot deviate from the protocol to learn more information. Hence, we need to ensure that the private circuit provider correctly performs an EP. This seeks the help of the so-called \emph{zero-knowledge argument of encrypted extended permutation} protocol. In this paper, we provide an improvement of this protocol. Our new protocol can be instantiated to be non-interactive while the previous protocol should be interactive. Meanwhile, compared with the previous protocol, our protocol is significantly (\eg more than $3.4\times$) faster, and the communication cost is only around $24\%$ of that of the previous one

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions