Privacy-preserving truth discovery, as a data aggregation algorithm that can extract reliable results from disparate and conflicting data in a privacy-preserving manner, has received a lot of attention in ensuring the reliability and privacy of data in mobile crowdsensing systems. However, most of the existing work requires that workers must stay online all the time during the full process of truth discovery. Although a few recent schemes have been proposed to tolerate worker dropout, they are tailored for a single-round setting. Repeating these schemes several times to adapt to the truth discovery will introduce significant computational and communication overheads, especially for the workers. To solve the above challenges, in this paper, we propose a multi-round efficient and secure truth discovery scheme in mobile crowdsensing systems that can balance the 3-way trade-off between privacy protection, dropout tolerance, and protocol efficiency. Specifically, we devise a novel mask generation capable of reusing secrets to eliminate the costly overhead of workers needing to recompute new secrets each round. Besides, we design a lightweight dropout tolerance mechanism to guarantee that even if workers drop out halfway, the server can still acquire meaningful truth. Rigorous security analysis and extensive experimental results demonstrate the privacy and efficiency of our scheme, respectively.

In this paper, we propose a privacy-preserving identity-based redactable blockchain (PIRB), the first identity-based redactable blockchain that supports flexible policies while maintaining accountability. Based on digital identities, PIRB enables a knowledge owner to set one policy for a batch of users while preserving policy privacy. Furthermore, similar to state-of-the-art solutions, PIRB draws inspiration from the proxy re-encryption technique to enforce user accountability. The design of PIRB entails addressing two primary technical challenges: firstly, achieving a flexible policy while upholding policy privacy; secondly, establishing accountability measures. To tackle the former challenge, we propose an enhanced identity-based encryption scheme that integrates polynomial function techniques. To address the latter challenge, a distinct identifier is generated for each user and subsequently concealed within the user’s secret key. Specifically, following existing schemes, we present the first scheme PIRB-I to cater to one-way access control scenarios, empowering owners to define access policies for designated editors. Additionally, recognizing the needs on the editor side for owner selection, we enhance PIRB-I through the introduction of matchmaking encryption, thereby supporting bilateral access control in a framework denoted as the second scheme PIRB-II. Notably, PIRB-I and PIRB-II involve a trade-off between computational and communication complexities. Specifically, when contrasted with PIRB-I, PIRB-II facilitates editors in owner selection, thereby mitigating editors’ communication overheads at the cost of increased computational overheads during policy generation and matching. Theoretical analysis demonstrates the inherent trade-off complexity and the resilience exhibited by PIRB-I and PIRB-II against chosen-plaintext attacks. Extensive experimentation on the FISCO blockchain shows that, compared with the state-of-the-art works, PIRB-I and PIRB-II achieve 200 times and 100 times computational efficiency improvements and 50 times and 60 times communication efficiency improvements on average, respectively.

Heterogeneous cloud-edge-terminal surveillance networks integrate resource-constrained cameras, edge devices, base stations, and cloud servers for security monitoring in smart campuses, transportation systems, and public spaces. Continuous full-color video uploading increases bandwidth pressure, enlarges the sensing pipeline’s attack surface, exposes privacy-sensitive background content, and may delay security analysis under congested edge links. To address these challenges, this paper presents AgentCam, a privacy-aware and security-oriented video sensing framework for heterogeneous surveillance networks. AgentCam combines a dual-mode grayscale-color camera with a cloud-hosted reinforcement learning (RL) agent. The sensing unit first transmits a low-cost grayscale observation; the cloud-side agent then selects security-relevant regions of interest (ROIs), and the terminal/edge unit applies the returned ROI action to transmit only selected color content. The cloud side reconstructs high-fidelity color video from grayscale frames and sparse color ROIs through restoration, temporal inference, and super-resolution. Experiments on surveillance-oriented video datasets show that AgentCam improves reconstruction quality over existing dual-mode reconstruction baselines while substantially reducing transmission overhead. These results indicate that selective ROI transmission can support scalable and privacy-aware security monitoring without continuously exposing full-color video streams. The framework is positioned as sensing-layer visual-data minimization rather than a complete cryptographic or adversarial-defense mechanism.