Abstract
Current physical-layer security techniques typically rely on a degraded eavesdropper, thus warranting some sort of advantage that can be relied upon to achieve higher levels of security. We consider instead non-degraded eavesdroppers that possess equal or better capabilities than legitimate receivers. Under this challenging setup, most of the current physical-layer security techniques become hard to administer and new dimensions to establish advantageous periods of communication are needed. For that, we consider employing a spread spectrum uncoordinated frequency hopping (UFH) scheme aided by friendly jammers for improved secrecy. We characterize the secrecy level of this spread spectrum scheme, by devising a stochastic geometry mathematical model to assess the secure packet throughput (probability of secure communication) of devices operating under UFH that accommodates the impact of friendly jammers. We further implement and evaluate these techniques in a real-world test-bed of software-defined radios. Results show that although UFH with jamming leads to low secure packet throughput values, by exploiting frequency diversity, these methods may be used for establishing secret keys. We propose a method for secret-key establishment that builds on the advantage provided by UFH and jamming to establish secret keys, notably against non-degraded adversary eavesdroppers that may appear in advantageous situations.

Abstract
We present a collision-free jammer selection policy for enhanced wireless secrecy. Jammers, selected from the neighbors of a source, are friendly in the sense that they are willing to help the source to transmit securely by causing interference/collisions to possible eavesdroppers. The proposed jammer selection policy results in the selection of the largest number of jammers that do not cause collisions among themselves. This enables jammers to assist the source to transmit securely by causing interference to eavesdroppers, while sending their own traffic into the network. © 2013 IEEE.

Abstract
With the recent advancements in wireless networks, Joint Communication and Sensing (JCAS) has become a growing field that is expected to be included in next-generation standards. However, not only is the current performance of the sensing ability still lacking to be used in real-world scenarios, proper security of such privacy-invasive technology has not been fully explored. To this end, we propose the creation of a more robust framework, capable of cross-domain detection and long-term analysis for improved detection, which will also serve as the basis for a security and privacy analysis of the threat landscape and solutions in this field.

Abstract

Abstract
Location Privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.