Federated Learning (FL) has become a crucial framework for collaboratively training Machine Learning (ML) models while ensuring data privacy. Traditional synchronous FL approaches, however, suffer from delays caused by slower clients (called stragglers), which hinder the overall training process. Specifically, in a synchronous setting, model aggregation happens once all the intended clients have submitted their local updates to the server. To address these inefficiencies, Buffered Asynchronous FL (BAsyncFL) was introduced, allowing clients to update the global model as soon as they complete local training. In such a setting, the new global model is obtained once the buffer is full, thus removing synchronization bottlenecks. Despite these advantages, existing Secure Aggregation (SA) techniques-designed to protect client updates from inference attacks-rely on synchronized rounds, making them unsuitable for asynchronous settings.

International audience

International audience

The Internet of Things (IoT) is a growing area of technology and has been identified as a key tool for enhancing industries' operation and performance. As IoT deployment rises worldwide, so do the threats; hence, security, especially authen-tication and integrity, is a critical consideration. One significant future threat is quantum attacks, which can only be defeated using Post-Quantum (PQ) cryptosystems. New Digital Signature (DS) standards for PQ security have been selected by the US National Institute of Standards and Technology (NIST). However, IoT comes with its own technical challenges from the constrained resources allocated to sensors and other similar devices. As a consequence, the use and suitability of these PQ schemes for IoT remains an open research area. In this paper, we identify an IoT architecture built from three distinct layers represented by a server, a gateway and an IoT device, respectively. We first test PQ DS scheme standards and compare them with current standards in order to assess their practicality for use in this architecture to provide authentication and integrity. Then, we select the most suitable PQ scheme at each layer according to the features of the corresponding device (server, gateway, IoT device) and the security property (authentication, integrity). We finally carry out experiments on our selection and provide an architectural model for making IoT communication and interaction PQ secure.

International audience

Blockchain is a type of distributed ledger.A wide range of consensus algorithms exists to reach consensus in a decentralized manner.However, most of them trade energy consumption for a degree of openness.Blockchains are primarily used for tokens and cryptocurrencies.Often the process of minting new tokens depends on actionable real world behaviors.A difficulty persists in securely translating said behavior into a decentralized blockchain.We formalize the generic concept of Proof of Behavior (PoB), and use it to create a consensus mechanism for generic permissionless blockchains.

The Internet of Things and its applications are becoming increasingly popular. Especially Smart Service Platforms like Alexa are in high demand. Such a platform retrieves data from sensors, processes them in a back-end, and controls actuators in accordance with the results. Thereby, all aspects of our everyday life can be managed. In this paper, we reveal the downsides of this technology by identifying its privacy threats based on a real-world application. Our studies show that current privacy systems do not tackle these issues adequately. Therefore, we introduce PSSST!, a user-friendly and comprehensive privacy system for Smart Service Platforms limiting the amount of disclosed private information while maximizing the quality of service at the same time.

The Internet of Things (IoT) technology has ex- panded widely across the world, promising new data management opportunities for industries, companies and individuals in different sectors, such as health services or transport logistics. The exponentially increasing number of IoT devices, their origin diversity, their limited capabilities in terms of resources, as well as the ever-increasing amount of data, raise new challenges for security and privacy protection, precluding traditional access control solutions to be integrated to this new environment. In this paper, we propose a reliable, server-aided, policy-based access control mechanism, named CHARIOT, that enables an IoT platform to verify credentials of different devices requesting access to the data stored within it. CHARIOT enables IoT devices to authenticate themselves to the platform without compromising their privacy by using attribute-based signatures. Our solution also allows secure delegation of costly computational operations to a cloud server, hence relieving the workload at IoT devices' side.

Ring signatures enable a user to anonymously sign a message on behalf of group of users. In this study, the authors propose the first ring signature scheme whose size is O (log 2 N ), where N is the number of users in the ring. They achieve this result by improving Chandran et al .’s ring signature scheme presented at the International Colloquium on Automata, Languages and Programming 2007. Their scheme uses a common reference string and non‐interactive zero‐knowledge proofs. The security of their scheme is proven without requiring random oracles.