Reliable communication is a fundamental distributed communication abstraction that allows any two nodes within a network to communicate with each other. It is necessary for more powerful communication primitives, such as broadcast and consensus. Using different authentication models, two classical protocols implement reliable communication in unknown and sufficiently connected networks. In the former, network links are authenticated, and processes rely on dissemination paths to authenticate messages. In the latter, processes generate digital signatures that are flooded throughout the network. This work considers the hybrid system model that combines authenticated links and authenticated processes. Additionally, we aim to leverage the possible presence of trusted nodes (e.g., network gateways) and trusted components (e.g., Intel SGX enclaves). We first extend the two classical reliable communication protocols to leverage trusted nodes. Then we propose DualRC, our most generic algorithm that considers the hybrid authentication model by manipulating dissemination paths and digital signatures, and leverages the possible presence of trusted nodes and trusted components. We describe and prove methods that establish whether our algorithms implement reliable communication on a given network.@en

Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies such as Continual Learning (CL). To enable research reproducibility, we propose a set of experimental best practices that precisely capture and emulate complex learning scenarios. To the best of our knowledge, our framework, Freddie, is the first entirely configurable framework for Federated Continual Learning (FCL), and it can be seamlessly deployed on a large number of machines leveraging containerization and Kubernetes. We demonstrate the effectiveness of Freddie on two use cases, (i) large-scale concurrent FL on CIFAR100 and (ii) heterogeneous task sequence on FCL, which highlight unaddressed performance challenges in FCL scenarios.@en

With the proliferation of Internet of Things (IoT) ecosystems, traditional resource orchestration mechanisms, executed on fog devices, encounter significant scalability, reliability and security challenges. To tackle these challenges, recent decentralized algorithms in Fog-IoT use Distributed Ledger Technologies to orchestrate resources and payments between peers. However, while distributed ledgers provide many desirable properties, their consensus mechanism introduces a performance bottleneck. This paper introduces Light-HIDRA, a consensus-less and decentralized resource orchestration system for Fog-IoT environments. At its core, Light-HIDRA uses Byzantine Reliable Broadcast (BRB) to coordinate actions without centralized control, therefore drastically reducing communication overhead and latency compared to consensus-based solutions. Light-HIDRA coordinates the scheduling and execution of workloads, and securely manages the payments that peers receive for dedicating resources to workloads. Light-HIDRA further increases performance and reduces overhead by grouping peers into distinct domains. We conduct an in-depth analysis of the protocol’s security properties, investigating its efficiency and robustness in diverse situations. We evaluate the performance of Light-HIDRA, highlighting its performance over HIDRA, a state-of-the-art baseline that uses smart contracts. Our experiments demonstrate that Light-HIDRA reduces the bandwidth usage by up to 57x, the latency of workload offloading by up to 142x, and shows superior throughput compared to HIDRA.@en

Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ (ICML '20) do not make such an honest majority assumption but can only be applied to scenarios where the server is provided with an auxiliary dataset used to filter malicious updates. FLAME (USENIX '22) and EIFFeL (CCS '22) maintain the semi-honest majority assumption to guarantee robustness and the confidentiality of updates. It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority. To tackle this problem, we propose a novel Byzantine-robust and privacy-preserving FL system, called MUDGUARD, to capture malicious minority and majority for server and client sides, respectively. Our experimental results demonstrate that the accuracy of MUDGUARD is practically close to the FL baseline using FedAvg without attacks (approximate 0.8% gap on average). Meanwhile, the attack success rate is around 0%-5% even under an adaptive attack tailored to MUDGUARD. We further optimize our design by using binary secret sharing and polynomial transformation leading to communication overhead and runtime decreases of 67%-89.17% and 66.05%-68.75%, respectively.

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Geo-replication provides disaster recovery after catastrophic accidental failures or attacks, such as fires, blackouts or denial-of-service attacks to a data center or region. Naturally distributed data structures, such as Blockchains, when well designed, are immune against such disruptions, but they also benefit from leveraging locality. In this work, we consolidate the performance of geo-replicated consensus by leveraging novel insights about hierarchical consensus and a construction methodology that allows creating novel protocols from existing building blocks. In particular we show that cluster confirmation, paired with subgroup rotation, allows protocols to safely operate through situations where all members of the global consensus group are Byzantine. We demonstrate our compositional construction by combining the recent HotStuff and Damysus protocols into a hierarchical geo-replicated blockchain with global durability guarantees. We present a compositionality proof and demonstrate the correctness of our protocol, including its ability to tolerate cluster crashes. Our protocol — Orion1 — achieves a 20% higher throughput than GeoBFT, the latest hierarchical Byzantine Fault-Tolerant (BFT) protocol.@en

Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single server. The scalability of such FL systems can be limited by two factors: server idle time due to synchronous communication and the risk of a single server becoming the bottleneck. In this paper, we propose a new FL architecture, Spyker, the first multi-server FL system that is entirely asynchronous, and therefore addresses these two limitations simultaneously. Spyker keeps both servers and clients continuously active. As in previous multi-server methods, clients interact solely with their nearest server, ensuring efficient update integration into the model. Differently, however, servers also periodically update each other asynchronously, and never postpone interactions with clients. We compare Spyker to three representative baselines - FedAvg, FedAsync and HierFAVG - on the MNIST and CIFAR-10 image classification datasets and on the WikiText-2 language modeling dataset. Spyker converges to similar or higher accuracy levels than previous baselines and requires 61% less time to do so in geo-distributed settings.

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Manipulation of user transactions by miners in permissionless blockchain systems is a growing concern. This problem is a pervasive and systemic issue that incurs high costs for users of decentralised applications and is known as Miner Extractable Value (MEV). Furthermore, transaction manipulations create other issues such as congestion, higher fees, and system instability. Detecting transaction manipulations is difficult, even though it is known that they originate from the pre-consensus phase of transaction selection for building blocks, at the base layer of blockchain protocols. In this paper, we summarize known transaction manipulation attacks. We present LO, an accountable base layer protocol designed to detect and mitigate transaction manipulations. LO is built around the accurate detection of transaction manipulations and assignment of blame at the granularity of a single mining node. LO forces miners to log all the transactions they receive into a secure mempool data structure and to process them in a verifiable manner. Overall, LO quickly and efficiently detects censorship, injection or re-ordering attempts. Our performance evaluation shows that LO is also practical and only introduces a marginal performance overhead.

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Byzantine consensus protocols aim at maintaining safety guarantees under any network synchrony model and at providing liveness in partially or fully synchronous networks. However, several Byzantine consensus protocols have been shown to violate liveness properties under certain scenarios. Existing testing methods for checking the liveness of consensus protocols check for time-bounded liveness violations, which generate a large number of false positives. In this work, for the first time, we check the liveness of Byzantine consensus protocols using the temperature and lasso detection methods, which require the definition of ad-hoc system state abstractions. We focus on the HotStuff protocol family that has been recently developed for blockchain consensus. In this family, the HotStuff protocol is both safe and live under the partial synchrony assumption, while the 2-Phase Hotstuff and Sync HotStuff protocols are known to violate liveness in subtle fault scenarios. We implemented our liveness checking methods on top of the Twins automated unit test generator to test the HotStuff protocol family. Our results indicate that our methods successfully detect all known liveness violations and produce fewer false positives than the traditional time-bounded liveness checks.@en

Current vehicular Intrusion Detection and Prevention Systems either incur high false-positive rates or do not capture zero-day vulnerabilities, leading to safety-critical risks. In addition, prevention is limited to few primitive options like dropping network packets or extreme options, e.g., ECU Bus-off state. To fill this gap, we introduce the concept of vehicular Intrusion Resilience Systems (IRS) that ensures the resilience of critical applications despite assumed faults or zero-day attacks, as long as threat assumptions are met. IRS enables running a vehicular application in a replicated way, i.e., as a Replicated State Machine, over several ECUs, and then requiring the replicated processes to reach a form of Byzantine agreement before changing their local state. Our study rides the mutation of modern vehicular environments, which are closing the gap between simple and resource-constrained "real-time and embedded systems", and complex and powerful "information technology" ones. It shows that current vehicle (e.g., Zonal) architectures and networks are becoming plausible for such modular fault and intrusion tolerance solutions—deemed too heavy in the past. Our evaluation on a simulated Automotive Ethernet network running two state-of-the-art agreement protocols (Damysus and Hotstuff) shows that the achieved latency and throughout are feasible for many Automotive applications.@en

Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on the aggregation method used, the local updates are either the gradients or the weights of local learning models, e.g., FedAvg aggregates model weights. Unfortunately, recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single mini- batch to reconstruct the private data used by clients during training. As the state-of-the-art reconstruction attacks solely focus on single update, realistic adversarial scenarios are over- looked, such as observation across multiple updates and updates trained from multiple mini-batches. A few studies consider a more challenging adversarial scenario where only model updates based on multiple mini-batches are observable, and resort to computationally expensive simulation to untangle the underlying samples for each local step. In this paper, we propose AGIC, a novel Approximate Gradient Inversion Attack that efficiently and effectively reconstructs images from both model or gradient updates, and across multiple epochs. In a nutshell, AGIC (i) approximates gradient updates of used training samples from model updates to avoid costly simulation procedures, (ii) leverages gradient/model updates collected from multiple epochs, and (iii) assigns increasing weights to layers with respect to the neural network structure for reconstruction quality. We extensively evaluate AGIC on three datasets, namely CIFAR-10, CIFAR- 100 and ImageNet. Our results show that AGIC increases the peak signal-to-noise ratio (PSNR) by up to 50% compared to two representative state-of-the-art gradient inversion attacks. Furthermore, AGIC is faster than the state-of-the-art simulation- based attack, e.g., it is 5x faster when attacking FedAvg with 8 local steps in between model updates. @en

DNA computing is an emerging field that aims at enabling more efficient data storage and processing. One principle of DNA computing is to encode some information (e.g., image, video, programming scripts) into a digital DNA-like sequence and then synthesize the corresponding DNA molecule. Synthesizing this molecule using digital or real human genomic fragments theoretically opens the possibility for privacy attacks, which have been demonstrated on a large array of human genomic data. These privacy attacks aim at breaching the privacy of DNA samples, allowing an attacker to discover privacy-critical information from the partial or complete DNA information of an individual. In the context of DNA computing, novel privacy attacks will certainly emerge and could consist in discovering a part of a particular script or video that is privacy-critical. It is therefore important to consider whether privacy attacks and defense mechanisms can be used when manipulating genomic data. First, this chapter provides the background about genomic data, and its modern generation and processing. It then provides a survey on known genomic privacy attacks, and presents the privacy-enhancing technologies that have been designed to protect genomic data. Later, this chapter also introduces the current trust management methods one can rely on to further secure DNA storage and processing methods, before discussing how DNA computing currently relates to those attacks and privacy-preserving technologies. Finally, this chapter presents future research avenues.

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Decentralised Autonomous Organisations (DAOs) have the capability of being a disruptive Web3 technology. Their usage of cryptographically secure distributed ledgers shows promise of replacing existing technical and financial intermediaries. However, this promise has not been fully materialised yet: existing attempts typically rely on centralisation as the required decentralised components do not exist or are not mature enough. We present our Web3 Deployment Experiment around a robust decentralised economy to address these issues. Our economy is unique due to the removal of all centralised components and governance. It is resilient against legal and economic attacks as no individual or organisation can compromise its functioning. We dub this characteristic extreme decentralisation. Similar to BitTorrent and Bitcoin, our extreme decentralisation DAOs carefully avoid single points of failure and are effectively unstoppable. Within our experiment around a music economy, we bypass all intermediaries in finance, technology, and the music industry itself with a direct donation to musicians. We demonstrate the viability of collective decision-making within our decentralised economy and present a set of principles for Web3 DAOs. Our implementation shows that the DAO ecosystem is fully deployable on smartphones, allowing anyone to create a DAO without reliance on central authorities or components.

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Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAScontinuously releases privacy-preserving and noise-free GWAS results as new genomes become available.@en

Self-Sovereign Identity (SSI) aspires to create a standardised identity layer for the Internet by placing citizens at the centre of their data, thereby weakening the grip of big tech on current digital identities. However, as millions of both physical and digital identities are lost annually, it is also necessary for SSIs to possibly be revoked to prevent misuse. Previous attempts at designing a revocation mechanism typically violate the principles of SSI by relying on central trusted components. This lack of a distributed revocation mechanism hampers the development of SSI. In this paper, we address this limitation and present the first fully distributed SSI revocation mechanism that does not rely on specialised trusted nodes. Our novel gossip-based propagation algorithm disseminates revocations throughout the network and provides nodes with a proof of revocation that enables offline verification of revocations. We demonstrate through simulations that our protocol adequately scales to national levels.@en

Nous considérons un réseau (modélisé par un graphe) utilisé pour propager des informations. Dans ce contexte, une source d’information diffuse à l’ensemble du réseau un message. Si la source est fiable, c’est à dire qu’elle envoie le même message à tous ses voisins directs, on souhaite qu’un nombre limité de participants malveillants qui tentent de miner sa crédibilité en retransmettant des messages sourcés contradictoires, ne puisse pas berner les participants hon- nêtes. Si la source est malveillante (et qu’elle cherche à augmenter l’entropie en envoyant tout et son contraire à ses voisins directs), les participants honnêtes doivent diminuer l’entropie des messages issus de la source, soit en les igno- rant, soit en délivrant un unique message (le même pour tous). Dans cet article, nous montrons que les méthodes dans la littérature pour résoudre ce problème peuvent être améliorées grâce à des optimisations spécifiques et inter-couches. Nos simulations montrent que ces optimisations peuvent être efficacement combinées pour diminuer la quantité totale d’informations transmises ou la latence du protocole. @en

Recently, streamlined Byzantine Fault Tolerant (BFT) consensus protocols, such as HotStuff, have been proposed as a means to circumvent the inefficient view-changes of traditional BFT protocols, such as PBFT. Several works have detailed trusted components, and BFT protocols that leverage them to tolerate a minority of faulty nodes and use a reduced number of communication rounds. Inspired by these works we identify two basic trusted services, respectively called the Checker and Accumulator services, which can be leveraged by streamlined protocols. Based on these services, we design Damysus, a streamlined protocol that improves upon HotStuff's resilience and uses less communication rounds. In addition, we show how the Checker and Accumulator services can be adapted to develop Chained-Damysus, a chained version of Damysus where operations are pipelined for efficiency. We prove the correctness of Damysus and Chained-Damysus, and evaluate their performance showcasing their superiority compared to previous protocols.

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Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that spans multiple institutional and legislative domains to securely compute GWAS results. However, these algorithms have limited applicability, since they still require a centralized instance to operate on the data and decide whether GWAS results can be safely disclosed, which violates privacy regulations, such as GDPR. In this work, we introduce GenDPR, a distributed middleware that leverages Trusted Execution Environments (TEEs) to securely determine a subset of the potential GWAS statistics that can be safely released. GenDPR achieves the same accuracy as centralized solutions, but requires transferring significantly less data because TEEs only exchange intermediary results but no genomes. Additionally, GenDPR can be configured to tolerate all-but-one honest-but-curious federation members colluding with the aim to expose genomes of correct members.@en

Federated Learning (FL) is a popular deep learning approach that prevents centralizing large amounts of data, and instead relies on clients that update a global model using their local datasets. Classical FL algorithms use a central federator that, for each training round, waits for all clients to send their model updates before aggregating them. In practical deployments, clients might have different computing powers and network capabilities, which might lead slow clients to become performance bottlenecks. Previous works have suggested to use a deadline for each learning round so that the federator ignores the late updates of slow clients, or so that clients send partially trained models before the deadline. To speed up the training process, we instead propose Aergia, a novel approach where slow clients (i) freeze the part of their model that is the most computationally intensive to train; (ii) train the unfrozen part of their model; and (iii) offload the training of the frozen part of their model to a faster client that trains it using its own dataset. The offloading decisions are orchestrated by the federator based on the training speed that clients report and on the similarities between their datasets, which are privately evaluated thanks to a trusted execution environment. We show through extensive experiments that Aergia maintains high accuracy and significantly reduces the training time under heterogeneous settings by up to 27% and 53% compared to FedAvg and TiFL, respectively.@en

The popularization of large-scale federated Genome-Wide Association Study (GWAS) where multiple data owners share their genome data to conduct federated analytics uncovers new privacy issues that have remained unnoticed or not given proper attention. Indeed, as soon as a diverse type of interested parties (e.g., private or public biocenters and governmental institutions from around the globe) and individuals from heterogeneous populations are participating in cooperative studies, interdependent and multi-party privacy appear as crucial issues that are currently not adequately assessed. In fact, in federated GWAS environments, the privacy of individuals and parties does not depend solely on their own behavior anymore but also on others, because a collaborative environment opens new credible adversary models. For instance, one might want to tailor the privacy guarantees to withstand the presence of potentially colluding federation members aiming to violate other members’ data privacy and the privacy deterioration that might occur in the presence of interdependent genomic data (e.g., due to the presence of relatives in studies or the perpetuation of previous genomic privacy leaks in future studies). In this work, we catalog and discuss the features, unsolved problems, and challenges to tackle toward truly end-to-end private and practical federated GWAS.@en

Vehicular social networking (VSN), as a novel communication paradigm, exploits opportunistic encounters among vehicles for mobile social networking, collaborative content dissemination, and to provide a variety of services for users and their vehicles. VSNs promise to solve problems such as the ever-increasing number of road accidents, or traffic congestion, by forming networks of vehicles whose users share common interests. However, in particular in urban regions, VSNs benefit from extending far beyond vehicular networks with their road side units, by integrating all kinds of sensors to provide higher accuracy and additional information in increasingly crowded areas. Integrating such sensors turns VSNs into networks of cellular-connected IoT devices with mobile cells and raises the question how to authenticate all these units in a scalable, efficient, and anonymous manner. In this article, we present SI-AKAV, an efficient group-local authentication scheme for cellular-connected IoT devices, which is specifically tailored to overcome the high computational costs of existing schemes, while improving in all of the above dimensions. For example, compared with the state-of-the-art authentication and key agreement protocol (AKA), SI-AKAV reduces communication volume by 29% and computation overhead by 41%.@en