Federated learning is a privacy-preserving distributed learning paradigm in which a server coordinates multiple clients to train a global model. However, current federated optimization introduces bias by favoring the interests of specific clients, overlooking the concerns of vulnerable participants to maximize global benefits. Many efforts have been made in the pursuit of fairness for this shortcoming, yet we notice that such endeavors exhibit extremely poor robustness. A minimal amount of malicious tampering is sufficient to disrupt convergence. In fact, we recognize a subtle trade-off between robustness and fairness, which remains an open question. To address these concerns, we propose FairRoP, a systematic strategy that enhances fairness and guarantees robustness with adaptive client selection. We model complex multi-objective optimization problems using a simple and efficient ϵ-greedy Thompson Sampling Multi-Armed Bandit (TS-MAB). At the core of this approach are three submodules:fairness awareness, attack detection, andq-Balance, each designed to tackle specific sub-problems within the broader optimization challenge. Our experimental results, conducted on real datasets, showcase that FairRoP significantly improves overall fairness and robustness compared to state-of-the-art solutions. Furthermore, our approach seamlessly integrates with other aggregation algorithms.

Software-based memory-erasure protocols are two-party communication protocols where a verifier instructs a computational device to erase its memory and send a proof of erasure. They aim at guaranteeing that low-cost IoT devices are free of malware by putting them back into a safe state without requiring secure hardware or physical manipulation of the device. Several software-based memory-erasure protocols have been introduced and theoretically analysed. Yet, many of them have not been tested for their feasibility, performance and security on real devices, which hinders their industry adoption. This article reports on the first empirical analysis of software-based memory-erasure protocols with respect to their security, erasure guarantees, and performance. The experimental setup consists of 3 modern IoT devices with different computational capabilities, 7 protocols, 6 hash-function implementations, and various performance and security criteria. Our results indicate that existing software-based memory-erasure protocols are feasible, although slow devices may take several seconds to erase their memory and generate a proof of erasure. We found that no protocol dominates across all empirical settings, defined by the computational power and memory size of the device, the network speed, and the required level of security. Interestingly, network speed and hidden constants within the protocol specification played a more prominent role in the performance of these protocols than anticipated based on the related literature. We provide an evaluation framework that, given a desired level of security, determines which protocols offer the best trade-off between performance and erasure guarantees.

The integrity and traceability of digital photographic evidence represent a critical factor during forensic investigations, especially when this evidence undergoes technical transformations, such as cropping or resolution enhancement. Ensuring that these modifications remain transparent, verifiable, and attributable is essential to maintaining the value of the evidence during an investigation. To meet these requirements, existing systems typically rely on blockchain-based implementations within permissioned networks or provide only limited support for image transformations. As a result, they often lack the flexibility and transparency required for open or decentralized forensic scenarios. In this paper, we propose an endorsement-based image forensics system that leverages public blockchain to record the lifecycle and verify the authenticity of images. Our system employs hybrid encryption to provide confidentiality of uploaded images while simultaneously ensuring that they remain auditable and non-repudiable. The system supports different trust models and enables users to assess the trustworthiness of an image’s provenance data directly and indirectly. Direct trust is achieved by validating an image transformation through reproducible functions or zero-knowledge proofs; indirect trust is enabled through publicly recorded endorsements. Our design achieves low gas costs and provides confidentiality, verifiability, and traceability guarantees, improving upon previous approaches without relying on permissioned infrastructures.

In recent years, the implementation of vehicle restricted areas (e.g., Low Emission Zones or Congestion Charge Zones) has proven effective in addressing urban traffic congestion and environmental pollution. Traditionally, these zones have been enforced using the highly centralized and infrastructure-expensive panoptic model. However, recent advancements in smart vehicle technology have enabled a shift towards decentralized, infrastructure-free approaches such as the lateral surveillance model. While offering significant advantages, this novel model also presents relevant security and privacy challenges, including potential misuse and widespread surveillance. This paper introduces a novel autonomous, decentralized, and infrastructure-free solution aligned with the lateral surveillance model. Leveraging Blockchain technology and privacy preservation measures, the system ensures user anonymity during fee pricing and payment processes, and it is capable of detecting and sanctioning dishonest drivers. To assess the applicability of the new scheme in real-world scenarios, it was implemented and tested in controlled environments as well as a low-traffic street. The evaluation included an assessment of the gas costs associated with the smart contracts and a detailed scalability analysis. The theoretical and experimental results demonstrate the feasibility of the proposed solution.

A Proof of Secure Erasure (PoSE) is a communication protocol where a verifier seeks evidence that a prover has erased the memory on a given device within the time frame of the protocol execution. Designers of PoSE protocols have long been aware that, if a prover can outsource the computation of the memory erasure proof to another device, then their protocols are trivially defeated. As a result, most software-based PoSE protocols in the literature assume that provers are isolated during the protocol execution, that is, provers cannot receive help from a network adversary. Our main contribution is to show that this assumption is not necessary. We introduce formal models for PoSE protocols playing against provers aided by external conspirators and develop two PoSE protocols that we prove secure in this context. We reduce the requirement of isolation to the more realistic requirement that the communication with the external conspirator is relatively slow. Software-based protocols with such relaxed isolation assumptions are especially pertinent for low-end devices, where it is too costly to deploy sophisticated protection methods.

Distance-bounding protocols are security protocols with a time measurement phase used to detect relay attacks, whose security is typically measured against mafia-fraud and distance-fraud attacks. A prominent subclass of distance-bounding protocols, known as lookup-based protocols, use simple lookup operations to diminish the impact of the computation time in the distance calculation. Independent results have found theoretical lower bounds 12nn2+1 and 12n, where n is the number of time measurement rounds, on the security of lookup-based protocols against mafia and distance-fraud attacks, respectively. However, it is still an open question whether there exists a protocol achieving both security bounds. This article closes this question in two ways. First, we prove that the two lower bounds are mutually exclusive, meaning that there does not exist a lookup-based protocol that provides optimal protection against both types of attacks. Second, we provide a lookup-based protocol that approximates those bounds by a small constant factor. Our experiments show that, restricted to a memory size that linearly grows with n, our protocol offers strictly better security than previous lookup-based protocols against both types of fraud.

Various modern protocols tailored to emerging wire-less networks, such as body area networks, rely on the proximity and honesty of devices within the network to achieve their security goals. However, there does not exist a security framework that supports the formal analysis of such protocols, leaving the door open to unexpected flaws. In this article we introduce such a security framework, show how it can be implemented in the protocol verification tool Tamarin, and use it to find previously unknown vulnerabilities on two recent key exchange protocols.

Abstract Active re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a.sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. Several formal privacy properties have been enunciated with the purpose of characterising the resistance of a graph against active attacks. However, anonymisation methods devised on the basis of these properties have so far been able to address only restricted special cases, where the adversaries are assumed to leverage a very small number of sybil nodes. In this paper, we present a new probabilistic interpretation of active re-identification attacks on social graphs. Unlike the aforementioned privacy properties, which model the protection from active adversaries as the task of making victim nodes indistinguishable in terms of their fingerprints with respect to all potential attackers, our new formulation introduces a more complete view, where the attack is countered by jointly preventing the attacker from retrieving the set of sybil nodes, and from using these sybil nodes for re-identifying the victims. Under the new formulation, we show thatk-symmetry, a privacy property introduced in the context of passive attacks, provides a sufficient condition for the protection against active re-identification attacks leveraging an arbitrary number of sybil nodes. Moreover, we show that the algorithmK-Match, originally devised for efficiently enforcing the related notion ofk-automorphism, also guaranteesk-symmetry. Empirical results on real-life and synthetic graphs demonstrate that our formulation allows, for the first time, to publish anonymised social graphs (with formal privacy guarantees) that effectively resist the strongest active re-identification attack reported in the literature, even when it leverages a large number of sybil nodes.

Verification of cryptographic protocols is traditionally built upon the assumption that participants have not revealed their long-term keys. However, in some cases, participants might collude to defeat some security goals, without revealing their long-term secrets.

In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication. Naive anonymisation of social network graphs often consists in deleting all identifying information of the users, while maintaining the original graph structure. Various types of attacks on naively anonymised graphs have been developed. Active attacks form a special type of such privacy attacks, in which the adversary enrols a number of fake users, often called sybils , to the social network, allowing the adversary to create unique structural patterns later used to re-identify the sybil nodes and other users after anonymisation. Several studies have shown that adding a small amount of noise to the published graph already suffices to mitigate such active attacks. Consequently, active attacks have been dubbed a negligible threat to privacy-preserving social graph publication. In this paper, we argue that these studies unveil shortcomings of specific attacks, rather than inherent problems of active attacks as a general strategy. In order to support this claim, we develop the notion of a robust active attack , which is an active attack that is resilient to small perturbations of the social network graph. We formulate the design of robust active attacks as an optimisation problem and we give definitions of robustness for different stages of the active attack strategy. Moreover, we introduce various heuristics to achieve these notions of robustness and experimentally show that the new robust attacks are considerably more resilient than the original ones, while remaining at the same level of feasibility.

Social network data is typically made available in a graph format, where users and their relations are represented by vertices and edges, respectively. In doing so, social graphs need to be anonymised to resist various privacy attacks. Among these, the so-called active attacks, where an adversary has the ability to enrol sybil accounts in the social network, have proven difficult to counteract. In this article, we provide an anonymisation technique that successfully thwarts active attacks while causing low structural perturbation. We achieve this goal by introducing $$(k, \Gamma _{G,\ell })$$ -adjacency anonymity: a privacy property based on $$(k,\ell )$$ -anonymity that alleviates the computational burden suffered by anonymisation algorithms based on $$(k,\ell )$$ -anonymity and relaxes some of its assumptions on the adversary capabilities. We show that the proposed method is efficient and establish tight bounds on the number of modifications that it performs on the original graph. Experimental results on real-life and randomly generated graphs show that when compared to methods based on $$(k,\ell )$$ -anonymity, the new method continues to provide protection from equally capable active attackers while introducing a much smaller number of changes in the graph structure.

Distance-bounding protocols are cryptographic protocols that securely establish an upper bound on the physical distance between the participants. Existing symbolic verification frameworks for distance-bounding protocols consider timestamps and the location of agents. In this work we introduce a causality-based characterization of secure distance-bounding that discards the notions of time and location. This allows us to verify the correctness of distance-bounding protocols with standard protocol verification tools. That is to say, we provide the first fully automated verification framework for distance-bounding protocols. By using our framework, we confirmed known vulnerabilities in a number of protocols and discovered unreported attacks against two recently published protocols.

A vertex cover of a graph G = ( V, E) is a set X ⊂ V such that each edge of G is incident to at least one vertex of X. The vertex cover number τ( G) is the minimum cardinality of a vertex cover of G. A dominating set D ⊆ V is a weakly connected dominating set of G if the subgraph G[ D] w = ( N[ D], E w ) weakly induced by D, is connected, where E w is the set of all edges having at least one vertex in D. The weakly connected domination number γ w ( G) of G is the minimum cardinality among all weakly connected dominating sets of G. In this article we characterize the graphs where γ w ( G) = τ( G). In particular, we focus our attention on bipartite graphs, regular graphs, unicyclic graphs, block graphs and corona graphs.

Distance-bounding protocols serve to thwart various types of proximity-based attacks, such as relay attacks. A particular class of distance-bounding protocols measures round trip times of a series of one-bit challenge-response cycles, during which the proving party must have minimal computational overhead. This can be achieved by precomputing the responses to the various possible challenges. In this paper we study this class of precomputation-based distance-bounding protocols. By designing an abstract model for these protocols, we can study their generic properties, such as security lower bounds in relation to space complexity. Further, we develop a novel family of protocols in this class that resists well to mafia fraud attacks.

Let G=(V,E) be a simple connected graph and S={w1,…,wt}⊆V an ordered subset of vertices. The metric representation of a vertex u∈V with respect to S is the t -vector r(u|S)=(dG(u,w1),…,dG(u,wt)) , where dG(u,v) represents the length of a shortest u−v path in G . A set S is a k -antiresolving set if k is the largest positive integer such that for every vertex v∈V−S there exist other k−1 different vertices v1,…,vk−1∈V−S such that v,v1,…,vk−1 have the same metric representation with respect to S . The k -metric antidimension of G is the minimum cardinality among all the k -antiresolving sets for G , and G is k -metric antidimensional if k is the largest integer such that G contains a k -antiresolving set. In this article, we provide characterizations for 1-metric antidimensional trees and unicyclic graphs, together with computationally efficient algorithms to decide whether these types of graphs are 1-metric antidimensional.

Contactless technologies such as radio-frequency identification, near field communication, and sensor networks are vulnerable to mafia and distance fraud. These types of fraud are aimed at successfully passing an authentication protocol by cheating on the actual distance between the prover and the verifier. Distance-bounding protocols have been designed to cope with these security issues, but none of them properly resist these two types of fraud without requiring additional memory and computation. The situation is even worse considering that just a few distance-bounding protocols are able to deal with the inherent background noise on the communication channels. This paper introduces a noise-resilient distance-bounding protocol that resists both mafia and distance fraud. The security of the protocol is analyzed against known attacks and illustrated by experimental results. The results demonstrate the significant advantage of the introduced lightweight design over previous proposals.