Federated learning (FL) has emerged as a privacy-preserving alternative to traditional machine learning, enabling collaborative model training without centralizing users’ data. While this decentralized approach enhances privacy, it introduces new trust assumptions. Specifically, that users honestly participate in the protocol. Simultaneously, the growing emphasis on fairness in machine learning has led to the development of fairness-aware FL schemes, which aim to ensure equitable model performance across diverse user groups. However, existing solutions for fair FL schemes rely on users truthfully reporting fairness-related metrics. This trust assumption opens the door to malicious behavior: users may manipulate these statistics to influence the global model, degrading its fairness, and/or its overall accuracy. Current frameworks lack mechanisms to detect or prevent such adversarial manipulation. This paper addresses this critical gap through two main contributions. First, as an example to illustrate our point, we empirically demonstrate the vulnerability of a representative fairness-aware FL framework to targeted attacks that exploit unverified fairness computations. Second, and most importantly, we propose a novel scheme (that can be integrated into existing fair FL schemes) that augments fairness-aware FL with verifiability. Our solution enables the detection of dishonest participants without compromising user privacy, thus strengthening the robustness of fairness-aware FL schemes.

In many machine learning scenarios, training occurs outside the control of the model sponsor or the entity using the model. A growing concern in such settings revolves around model poisoning and data poisoning-how training is conducted and which data contributes to the process. This paper introduces a protective scheme against model and data poisoning attacks. Leveraging cryptographic primitives such as hashes, signature schemes, and zero-knowledge proofs, the scheme ensures the integrity of the training process. Hashing maintains the continuity of data from authenticated sensors, while signatures validate the data. In the end, zero-knowledge proofs verify the correct model computation by the entity carrying out the training process. By adopting this approach, model sponsors can securely delegate training tasks, guaranteeing the authenticity of the results. Implementation and testing demonstrate the scheme's feasibility, effectively countering data and model poisoning threats.

Summary Since digital artworks are indeed digital content, they face the inherent problem digital content has: the link between content and its original author is very difficult to keep. Additionally, retaining control over digital copies of the content is also a challenging task. Digital coins have solved this very same problem through cryptocurrencies (for instance, Bitcoin) and have opened the door to apply the same techniques to other similar scenarios where ownership of digital assets needs to be preserved. In this paper, we propose BArt, a transparent and distributed mechanism for artists to commercialize their digital artwork, keeping control of the copies, monetizing its usage, and maintaining ownership. BArt allows artists to publicly register their work in the Bitcoin blockchain and sell usage rights in exchange for bitcoins. Buyers are allowed to exert the acquired rights. Proper behavior from all parties is enforced by the system with cryptography and economic incentives.

The Lightning Network is a second layer technology running on top of Bitcoin and other Blockchains. It is composed of a peer-to-peer network, used to transfer raw information data. Some of the links in the peer-to-peer network are identified as payment channels, used to conduct payments between two Lightning Network clients (i.e., the two nodes of the channel). Payment channels are created with a fixed credit amount, the channel capacity. The channel capacity, together with the IP address of the nodes, is published to allow a routing algorithm to find an existing path between two nodes that do not have a direct payment channel. However, to preserve users' privacy, the precise balance of the pair of nodes of a given channel (i.e. the bandwidth of the channel in each direction), is kept secret. Since balances are not announced, second-layer nodes probe routes iteratively, until they find a successful route to the destination for the amount required, if any. This feature makes the routing discovery protocol less efficient but preserves the privacy of channel balances. In this paper, we present an attack to disclose the balance of a channel in the Lightning Network. Our attack is based on performing multiple payments ensuring that none of them is finalized, minimizing the economical cost of the attack. We present experimental results that validate our claims, and countermeasures to handle the attack.

This paper presents a classifier architecture that is able to deal with classification of interlinked entities when the only information available is the existing relationships between these entities, i.e. no semantic content is known for either the entities or their relationships. After proposing a classifier to deal with this problem, we provide extensive experimental evaluation showing that our proposed method is sound and that it is able to achieve high accuracy, in most cases much higher than other already existing algorithms configured to tackle this very same problem. The contributions of this paper are twofold: first, it presents a classifier for interlinked entities that outperforms most of the existing algorithms when the only information available is the relationships between these entities; second, it reveals the power of using label independent (LI) features extracted from network structural properties in the bootstrapping phases of relational classification.

In this paper, we consider the problem of anonymization on large networks. There are some anonymization methods for networks, but most of them can not be applied on large networks because of their complexity. We present an algorithm for k-degree anonymity on large networks. Given a network G, we construct a k-degree anonymous network, G, by the minimum number of edge modifications. We devise a simple and efficient algorithm for solving this problem on large networks. Our algorithm uses univariate micro-aggregation to anonymize the degree sequence, and then it modifies the graph structure to meet the k-degree anonymous sequence. We apply our algorithm to a different large real datasets and demonstrate their efficiency and practical utility.

Web crawlers are complex applications that explore the Web for different purposes. Web crawlers can be configured to crawl online social networks (OSNs) to obtain relevant data about their global structure. Before a web crawler can be launched to explore the Web, a large amount of settings have to be configured. These settings define the crawler's behavior and they have a big impact on the collected data. Both the amount of collected data and the quality of the information that it contains are affected by the crawler settings and, therefore, by properly configuring these web crawler settings we can target specific goals to achieve with our crawl. In this paper, we review the configuration choices that an attacker who wants to obtain information from an OSN by crawling it has to make to conduct his attack. We analyze different scheduler algorithms for web crawlers and evaluate their performance in terms of how useful they are to pursue a set of different adversary goals.

JXTA defines a set of six core protocols specifically suited for ad hoc, pervasive, multi-hop, peer-to-peer (P2P) computing. These protocols allow peers to cooperate and form autonomous peer groups. This paper presents a method that provides security services to the core protocols: privacy, authenticity, integrity and non-repudiation. The presented mechanisms are fully distributed and based on a pure peer-to-peer model, not requiring the arbitration of a trusted third party or a previously established trust relationship between peers, which is one of the main challenges under this kind of environments.

In order to access resources within a peer-to-peer network, JXTA completely relies in the usage of advertisements published by the resource owner. This may easily lead to DoS or spoofing attacks from malicious peers by forging advertisements with false identifiers unless advertisement authenticity is provided. Furthermore, in a fully distributed environment, in order to truly isolate a malicious node, collaboration is necessary from other peers. For that reason, it must be possible to prove to other parties that a peer is being disruptive. This paper presents a method that provides authenticity and non-repudiation to JXTA advertisements. This scheme is fully distributed and based in a pure peer-to-peer model, not requiring the arbitration of a trusted third party or a previously established trust relationship between peers, which is one of the main challenges under this kind of environments.

Secure electronic exams are one of the most difficult challenges in e-learning security. The relevance of the examination process for any academic institution implies that different security mechanisms must be applied in order to preserve some security properties during different examination stages. In this paper, we present a secure e-exam management system where all exam related information is in digital format. We propose a cryptographic scheme that has to be executed in order to achieve the desired security levels at every exam stage.

Peer-to-peer applications enable users to create a communications framework from scratch without the need of a central service provider. This is achievable via the aggregation of resources each one of them provide, creating a completely distributed collaborative environment. Under some circumstances, groups of users operating in a global peer-to-peer network may need to create a closed communities, limiting access to the shared resources only to its members. This can be useful for security reasons or in order to provide scoping within the global overlay network. In order to achieve this scenario, security mechanisms must be implemented. In this paper, a method for peer group access control is presented managed only by the group members in a completely autonomous way without the need of any third parties

This paper describes a study regarding the influence of a watermarking scheme based on near-lossless JPEG2000 compression in the performance of lossless or near-lossless image compression methods. Due to the nature of ultraspectral imaging applications, the imperceptibility property becomes more important than capacity or robustness, which determines the design of the watermarking scheme. Four strategies for band watermarking are discussed, depending on which bands are selected for embedding the mark and how. Preliminary results show that both intra-granule and inter-granule band capacity and imperceptibility are very variable, thus an adaptive watermarking band strategy must be used. On the other hand, the proposed watermarking scheme does not alter the obtained compression ratio for watermarked images when compared to the original ones, for a reasonable capacity and an imperceptible quality loss.