Elisa Bertino

5G transmits System Information Blocks (SIBs) without authentication during bootstrapping, enabling fake base station attacks. Direct NIST-PQC integration is infeasible: ML-DSA requires 34 fragmented packets and up to 5,282 ms delay. We present a two-phase PQ migration framework. Phase I provides transitional security via hierarchical identity-based threshold signatures with a fail-stop mechanism backed by PQ-secure audit logging. This transitional solution requires only 144 B overhead and 2.99 ms end-to-end delay, which is three orders of magnitude faster than ML-DSA. Phase II achieves full PQ authentication via a TESLA-style HMAC chain anchored by a compact post-quantum signature, which is applied once per epoch (160.07 ms delay). Both phases fit within 1-2 SIB1 packets and are validated on a real over-the-air 5G testbed.

The 5G protocol lacks a robust base station (BS) authentication mechanism during the initial bootstrapping phase, leaving it susceptible to fake BSs, spoofed broadcasts, and large-scale manipulation of System Information Blocks (SIBs). Existing solutions incur high communication overhead, rely on centralized trust, and lack accountability and long-term breach resiliency. Given the inevitability of BS compromise and the severe impact of forged SIBs as the root of trust (e.g., fake alerts, tracking, false roaming), distributed trust, verifiable forgery detection, and audit logging are essential yet remain largely unexplored. These challenges are further amplified by the emergence of quantum-capable adversaries. While NIST Post-Quantum Cryptography (PQC) standards are widely viewed as a path toward long-term security, their feasibility under 5G's strict packet-size, latency, and broadcast constraints has not been systematically studied. This work presents, to our knowledge, the first comprehensive network-level performance characterization of integrating NIST-PQC standards and conventional digital signatures into 5G BS authentication, showing that direct PQC adoption is impractical due to excessive signature sizes, fragmentation, and protocol-level delays. To address these challenges, we propose BORG, a future-proof authentication framework based on a Hierarchical Identity-Based Threshold Signature with Fail-Stop (HITFS) properties. BORG distributes trust across multiple BSs via threshold signing, enables post-mortem verifiable forgery detection, and provides tamper-evident, PQ-secure audit logging, while maintaining compact signatures that fit within a single SIB1 packet without fragmentation and incurring minimal UE overhead, as validated through our real over-the-air 5G testbed implementation.

The development of mobile web services has brought significant attention to spatial crowdsourcing. The uneven distribution of tasks and workers has led to recent research on Cross-Platform Spatial Crowdsourcing (CPSC), aiming for a multi-win situation for platforms, workers, and task requesters. Previous studies on CPSC problems focused on task assignment and worker selection performance, overlooking the importance of privacy preservation. This article addresses the existing challenges of privacy preservation and service quality by formulating a Privacy-Preserving Cross-Platform Spatial Crowdsourcing (PP-CPSC) problem and proves it to be NP-hard. We propose an Evolutionary Differential Privacy (Evo-DP) approach to optimize PP-CPSC. Evo-DP’s evolutionary framework enables efficient and flexible optimization of privacy budget allocation. Within Evo-DP, each solution to the privacy budget allocation is represented as an individual in the population. To approximate the optimal solution, three evolutionary operations—mutation, crossover, and scaling—are employed for population updates, along with a selection process. A hybrid population model is introduced to balance exploration and exploitation abilities. Experimental results demonstrate Evo-DP’s superiority over previous strategies in terms of solution quality, convergence speed, and scalability.

Federated learning (FL) has shown great potential, especially with the rise of complex foundation models and growing privacy needs. FL has experienced challenges, including high communication costs, privacy concerns of user data, and the complexities of non-independent and identically distributed (non-IID) data. This paper aims to address these challenges through our proposed FL framework called Adaptive Pruning FL (AdpFL). AdpFL conducts an exploration phase prior to the formal FL process, during which each local dataset's deviation and corresponding threshold are computed locally. These results are then utilised to perform model pruning during the formal FL training. By doing so, the method achieves full adaptivity while simultaneously addressing challenges related to communications costs and non-IID data. Meanwhile, AdpFL enhances pruning by replacing part of the parameters with zeros, thereby preventing membership inference attacks (MIA) and ensuring privacy in both local and global models. The extensive experiments on various datasets and FL tasks demonstrate that our method not only efficiently accelerates global convergence but also achieves superior accuracy and robustness compared to traditional methods. AdpFL reduces computational overhead and decreases the effective update size (i.e., the proportion of non-zero parameters) by approximately half while improving global accuracy by up to 20%, and it also lowers the accuracy of white-box MIA attacks across various models. These improvements make FL more efficient, scalable, and practical in real-world situations.