Abstract
In this letter, we introduce the computation-limited (comp-limited) signals, a communication capacity regime where the computational complexity of signal processing is the primary constraint for communication performance, overriding factors such as power or bandwidth. We present the Spectro-Computational (SC) analysis, a novel mathematical framework designed to enhance classic concepts of information theory -such as data rate, spectral efficiency, and capacity - to accommodate the computational complexity overhead of signal processing. We explore a specific Shannon regime where capacity is expected to increase indefinitely with channel resources. However, we identify conditions under which the time complexity overhead can cause capacity to decrease rather than increase, leading to the definition of the comp-limited signal regime. Furthermore, we provide examples of SC analysis and demonstrate that the Orthogonal Frequency Division Multiplexing (OFDM) waveform falls under the comp-limited regime unless the lower-bound computational complexity of the N-point Discrete Fourier Transform (DFT) problem verifies as ohm (N)$ , which remains an open challenge in the theory of computation.

Abstract
In an era dominated by Location-Based Services (LBSs), the concern of preserving location privacy has emerged as a critical challenge. To address this, Location Privacy-Preserving Mechanisms (LPPMs) were proposed, in where an obfuscated version of the exact user location is reported instead. Adding to noise-based mechanisms, location discretization, the process of transforming continuous location data into discrete representations, is relevant for the efficient storage of data, simplifying the process of manipulating the information in a digital system and reducing the computational overhead. Apart from enabling a more efficient data storage and processing, location discretization can also be performed with privacy requirements, so as to ensure discretization while providing privacy benefits. In this work, we propose a Privacy-Aware Remapping mechanism that is able to improve the privacy level attained by Geo-Indistinguishability through a tailored pre-processing discretization step. The proposed remapping technique is capable of reducing the re-identification risk of locations under Geo-Indistinguishability, with limited impact on quality loss.

Abstract
The advent of 6G networks is anticipated to introduce a myriad of new technology enablers, including heterogeneous radio, RAN softwarization, multi-vendor deployments, and AI-driven network management, which is expected to broaden the existing threat landscape, demanding for more sophisticated security controls. At the same time, privacy forms a fundamental pillar in the EU development activities for 6G. This decentralized and globally connected environment necessitates robust privacy provisions that encompass all layers of the network stack. In this paper, we present PRIVATEER’s approach for enabling “privacy-first” security enablers for 6G networks. PRIVATEER aims to tackle four major privacy challenges associated with 6G security enablers, i.e., i) processing of infrastructure and network usage data, ii) security-aware orchestration, iii) infrastructure and service attestation and iv) cyber threat intelligence sharing. PRIVATEER addresses the above by introducing several innovations, including decentralised robust security analytics, privacy-aware techniques for network slicing and service orchestration and distributed infrastructure and service attestation mechanisms.

Abstract

Abstract
Nowadays, Virtual Reality (VR) and Augmented Reality (AR) systems are not exclusively associated with the gaming industry. Their potential is also useful for other business areas such as healthcare, automotive, and educational domains. Companies need to accompany technological advances and enhance their business processes and thus, the adoption of VR or AR technologies could be advantageous in reducing resource usage or improving the overall efficiency of processes. However, before implementing these technologies, companies must be aware of potential cyberattacks and security risks to which these systems are subject. This study presents a survey of attacks related to VR and AR scenarios and their risk assessment when considering healthcare, automation, education, and gaming industries. The main goal is to make companies aware of the possible cyberattacks that can affect the devices and their impact on their business domain.

Abstract
Traditional identity management (IdM) systems rely on third-party identity providers (IdPs) and are centralised, which can make them vulnerable to data breaches and other security risks. Self-sovereign identity (SSI) is a newer IdM model that allows users to control their own identities by using decentralised technologies like blockchain to store and verify them. However, SSI systems have their own security concerns, such as digital wallet vulnerabilities, blockchain threats and conflicts with general data protection regulation (GDPR). Additionally, the lack of incentives for issuers, verifiers and data owners could limit its acceptance. This paper proposes SPIDVerify, which is a decentralised identity verification framework that utilises an SSI-based architecture to address these issues. The framework uses a mixed method for acquiring a W3C standard verified credentials and to ensure that only a thoroughly verified entity acquires verified credential, and employs secure key cryptographic protocols; Diffie-Hellman (DH) and Extended Triple Diffie-Hellman (X3DH) for forward secrecy secure communication, single-use challenge-response for authentication, and Swarm network for decentralised storage of data. These methods enhance the security of the proposed framework with better resilience against impersonation and credential stealing. To evaluate the proposal, we have outlined the limitations in related works and demonstrated two scenarios to showcase the strength and effectiveness of SPIDVerify in dealing with the threats identified. We have also tested the methods used in SPIDVerify by measuring the time taken to execute certain processes.