The rapid growth of Industrial Internet of Things (IIoT) systems has heightened the need for secure cryptographic operations, particularly multi-party digital signatures for decentralized trust. However, existing multi-party signature schemes are vulnerable to insider attacks, and no current solutions address insider-induced data exfiltration effectively. Cryptographic Reverse Firewalls (CRFs) provide a promising solution but face challenges in integration with digital signatures, especially with hash-dependent components. We propose MCRF, a CRF-enhanced multi-party signature scheme designed for IIoT environments. MCRF uses a commitment-based mechanism to optimize the signing process, enabling output-side CRF to re-randomize signatures without compromising correctness. The two-stage CRF architecture-input-side CRF for sanitizing messages and output-side CRF for re-randomizing signatures-ensures efficient protection against both input-triggered and output-stealth exfiltration attacks. MCRF offers strong leakage resistance with minimal computational and communication overhead, making it a scalable solution for secure multi-party signing in IIoT systems.

As a promising subdomain of intelligent transportation systems (ITS), Internet of Vehicles (IoV) can be empowered by digital twin (DT) technology for real-time traffic simulation and artificial intelligence (AI)-driven predictive analytics in evolutionary trend projection, demonstrating significant potential in dynamic transportation optimization. Among various machine learning paradigms, federated learning (FL) not only aligns well with IoV, but also provides it with privacy protection. Traditional FL faces single point of failure due to the existence of an aggregation center, so blockchain-based FL with multiple aggregators is utilized to mitigate this issue. Nevertheless, in such distributed environments, both aggregators and model parameters exposed to network are vulnerable to attacks, impeding the normal operation of FL. In this paper, for blockchain-enabled FL with multiple aggregators in IoV, we propose CLTSwNI&IA, the first non-interactive certificateless threshold signature with identifiable abort. This scheme eliminates certificate management and key escrow, adopts a blockchain-oriented approach by utilizing a fully distributed signing paradigm. Additionally, the proposed signing scheme is capable of identifying malicious FL aggregators during the entire process through distributed fine-grained verification and ensuring the integrity of aggregation results. Finally, theoretical and experimental comparisons with related literature demonstrate the advanced functionality and the acceptable efficiency of our approach.

This reproducibility companion paper provides implementation details of our paper ''Learning differentiable particle filter on the fly''[10] presented at the 57th Asilomar Conference on Signals, Systems, and Computers. We provide detailed documentation to replicate our research, which proposes a differentiable particle filter capable of online learning. This paper includes our Python code repository, experimental configurations, dataset description, and step-by-step instructions to reproduce the results. By sharing these resources, we aim to encourage open source and further research in this direction.

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel G raph neural news R ecommendation model with user E xisting and P otential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bi-directional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.

Participant selection is a fundamental research issue in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long periods of candidate participants’ historical mobility trajectories are available to model their patterns before the selection process, which is not realistic for some new MCS applications or platforms. The sparsity or even absence of mobility traces will incur inaccurate location prediction, thus undermining the deployment of new MCS applications. To this end, this paper investigates a novel problem called “From-Scratch MCS” (FS-MCS for short), in which we study how to intelligently select participants to minimize such a “cold-start” effect. Specifically, we propose a novel framework based on reinforcement learning, named RL-Recruiter+. With the gradual accumulation of mobility trajectories over time, RL-Recruiter+ is able to make a good sequence of participant selection decisions for each sensing slot. Compared to its previous version, RL-Recruiter, Re-Recruiter+ jointly considers both the previous coverage and current mobility predictability when training the participant selection decision model. We evaluate our approach experimentally based on two real-world mobility datasets. The results demonstrate that RL-Recruiter+ outperforms the baseline approaches, including RL-Recruiter under various settings.

Participant selection is a major research challenge in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long and fixed periods of candidate participants’ historical mobility trajectories are available before the selection process. This enables the frameworks to accurately model mobility which enables the optimization of selection. However, this assumption may not be realistic for newly-released MCS applications or platforms because the candidates have just boarded without previous mobility profiles. The sparsity or even absence of mobility traces will incur inaccurate location prediction of the individual participant, thus imposing negative effects on the participant selection process and hindering the practical deployment of new MCS applications. To this end, this paper investigates a novel problem called "From-Scratch MCS" (FS-MCS for short), in which we study how to intelligently select participants to minimize such "cold-start" effect. Specifically, we propose a novel framework based on reinforcement learning, which we name RL-Recruiter. With the gradual accumulation of mobility trajectories over time, RL-Recruiter can make a good sequence of participant selection decisions for each sensing slot by incrementally extracting and utilizing the collective mobility patterns of all candidate participants, thus avoiding the prediction of individual participant’s location that is very inaccurate when the training data is sparse. We test our approach experimentally based on two real-world mobility datasets. Our experiment results demonstrate that RL-Recruiter outperforms the baseline approaches under various settings.