Pseudonym certificates play a crucial role in providing authorized access in vehicular networks with fine-grained privacy demands. However, the revocation of pseudonym certificates in large-scale, resource-constrained context has posed a significant challenge. The global revocation status is susceptible to disruption by localized anomalous events. Moreover, as the scale of revocation grows, the synchronization process becomes increasingly vulnerable to attacks and incurs high overhead. To decouple the global revocation status from local revocation events, we propose a spatial-temporal pseudonym revocation and verification framework that supports batch pseudonym revocation within resilient revocation cycles. Revoked pseudonyms within the event area are locally filtered, while other pseudonyms are characterized by maintaining a proof to attest their consistent and legitimate status. To improve the efficiency of revocation status updates and mitigate centralization risks, a distributed caching and proof update strategy assisted by edge nodes is presented. Extensive simulations based on real-world large-scale datasets demonstrate that PV-STR scheme significantly reduces the vulnerability window, communication overhead, and memory consumption compared to state-of-the-art approaches.

A cryptographic implementation produces very similar power leakages when fed with the same input. Side-channel collision attacks exploit these similarities to establish the relationship between sub-keys and improve the efficiency of key recovery. Benefiting from independence of leakage model, they play an important role in non-profiled setting. However, performance of existing approaches against single collision value is still sub-optimal and optimization is promising. Motivated by this, we first theoretically analyze the mathematical dependency between the number of collisions and the number of encryptions, and propose an efficient side-channel attack named Collision-Paired Correlation Attack (CPCA) to guarantee that the side with fewer samples in a collision is completely paired in low noise scenario. This allows overcoming the inefficient utilization of information in existing works. Moreover, to further employ underlying informativeness, we maximize collision pairs as many as possible. This optimization significantly improves performance of CPCA and thereby extends it to large noise scenarios. Finally, to achieve moderate computational complexity, two equivalent variants of CPCA are investigated to address the potential problem of limited computing resources. Our further theoretical study illustrates that CPCA provides the upper security bound of Correlation-Enhanced Collision Attack (CECA), and experimental results fully verify its superiority.

Efficient data collection has been treated as a key challenge especially in the sparsely deployed Internet of Things (IoT) networks. Compared with the conventional data collection methods using static sinks, mobile data collectors (MDCs) are considered as a more efficient approach where MDCs transfer data from sensors to access points (APs) by roaming over different geographical regions. In this work, we propose an analytical framework to study the coverage performance of IoT with MDCs where MDCs follow a simple random waypoint (SRWP) mobility model. To characterize the interference distribution of the whole network, we first derive exact expressions for the average contact time (CT) and inter-contact time (ICT) between a typical sensor and its associated MDC. Then we determine the active probability of the typical sensor by using the derived CT and ICT. The coverage probability is finally derived by taking into account the communication range of sensors, velocity of MDCs, density of sensors and MDCs, and the SINR threshold. Our results reveal the fact that the velocity of MDCs has little effect on coverage probability while a higher velocity can significantly lower the end-to-end delay.