Critical infrastructures have to withstand advanced and persistent threats, which can be addressed using Byzantine fault tolerant state-machine replication (BFT-SMR). In practice, unattended cyberdefense systems rely on threat level detectors that synchronously inform them of changing threat levels. However, to have a BFT-SMR protocol operate unattended, the stateof- the-art is still to configure them to withstand the highest possible number of faulty replicas f they might encounter, which limits their performance, or to make the strong assumption that a trusted external reconfiguration service is available, which introduces a single point of failure. In this work, we present ThreatAdaptive the first BFT-SMR protocol that is automatically strengthened or optimized by its replicas in reaction to threat level changes. We first determine under which conditions replicas can safely reconfigure a BFT-SMR system, i.e., adapt the number of replicas n and the fault threshold f, so as to outpace an adversary. Since replicas typically communicate with each other using an asynchronous network they cannot rely on consensus to decide how the system should be reconfigured. ThreatAdaptive avoids this pitfall by proactively preparing the reconfiguration that may be triggered by an increasing threat when it optimizes its performance. Our evaluation shows that ThreatAdaptive can meet the latency and throughput of BFT baselines configured statically for a particular level of threat, and adapt 30% faster than previous methods, which make stronger assumptions to provide safety. @en

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

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

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

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

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

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

ederated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations. Such collected data is increasingly non-independent and non- identically distributed (non-IID), negatively affecting training accuracy. Previous works tried to mitigate the effects of non- IID datasets on training accuracy, focusing mainly on non-IID labels, however practical datasets often also contain non-IID features. To address both non-IID labels and features, we propose FedGMCC1, a novel framework where a central server aggregates client models that it can cluster together. FedGMCC clustering relies on a Monte Carlo procedure that samples the output space of client models, infers their position in the weight space on a loss manifold and computes their geometric connection via an affine curve parametrization. FedGMCC aggregates connected models along their path connectivity to produce a richer global model, incorporating knowledge of all connected client models. FedGMCC outperforms FedAvg and FedProx in terms of convergence rates on the EMNIST62 and a genomic sequence classification datasets (by up to +63%). FedGMCC yields an improved accuracy (+4%) on the genomic dataset with respect to CFL, in high non-IID feature space settings and label incongruency.@en

ederated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations. Such collected data is increasingly non-independent and non- identically distributed (non-IID), negatively affecting training accuracy. Previous works tried to mitigate the effects of non- IID datasets on training accuracy, focusing mainly on non-IID labels, however practical datasets often also contain non-IID features. To address both non-IID labels and features, we propose FedGMCC1, a novel framework where a central server aggregates client models that it can cluster together. FedGMCC clustering relies on a Monte Carlo procedure that samples the output space of client models, infers their position in the weight space on a loss manifold and computes their geometric connection via an affine curve parametrization. FedGMCC aggregates connected models along their path connectivity to produce a richer global model, incorporating knowledge of all connected client models. FedGMCC outperforms FedAvg and FedProx in terms of convergence rates on the EMNIST62 and a genomic sequence classification datasets (by up to +63%). FedGMCC yields an improved accuracy (+4%) on the genomic dataset with respect to CFL, in high non-IID feature space settings and label incongruency.@en