FSC-CD: A Feature–Structure Coupled Approach for Community Deception in Networks

Global community deception via a cooperative evolutionary genetic algorithm based on an elite population

Comprehensive, Efficient Large-Scale Community Detection via Structural Entropy Game

A novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional structural entropy within a potential game framework, and introduces a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.

ESecDT: Communication-Efficient and Secure Decision Tree Training Framework

Decision trees are widely used in various domains, such as user behavior analysis and financial risk assessment. Recently, increasing concerns about data privacy have driven the development of secure decision tree training frameworks. We propose ESecDT, a novel framework that enables collaborative decision tree training while preserving participants’ training data. ESecDT integrates the computational advantages of Function Secret Sharing (FSS) and Replicated Secret Sharing (RSS) through a co-design approach. We first introduce new protocols based on this co-design for tree training building blocks, including GroupSum, GroupPrefixSum, and VecMMax. Subsequently, we design a bit-width-aware training framework that manages data with different bit-widths and supports FSS key reuse. These designs enable ESecDT to ensure strong privacy guarantees, preserving the training data and all intermediate variables throughout the training process, while maintaining practical efficiency. Extensive experiments on nine real-world and synthetic datasets demonstrate that ESecDT reduces online communication overhead by <inline-formula> <tex-math notation="LaTeX">$4.25\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$5.56\times $ </tex-math></inline-formula> versus state-of-the-art frameworks. In the WAN setting, ESecDT achieves <inline-formula> <tex-math notation="LaTeX">$1.78\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$4.48\times $ </tex-math></inline-formula> speedup in training runtime and completes a 30,000-sample training task in less than half an hour, demonstrating strong potential for practical deployment.

Optimizing multi-objective strategies for enhanced Tor De-anonymization

Tor employs multi-layer encryption and three-hop circuits to provide low-latency anonymity. While indispensable for privacy, these same properties can also be misused to conceal illicit activity. This dual-use nature makes effective de‑anonymization essential under appropriate, policy-bounded oversight, so that harmful behavior can be uncovered without undermining legitimate use. Yet de‑anonymization is not free: taking nodes offline and deploying honeypots consumes significant resources, increases exposure, and risks degrading network availability. Prior work faces two limitations: (i) it decouples the choice of which node to target from which method to apply, overlooking their strong coupling; and (ii) it often evaluates effectiveness with narrow, single-effect proxies, neglecting collateral network impact and operational cost. To support better de‑anonymization, we model joint node–technique selection as a tri-objective problem balancing attack gain (AP), attack impact (AI), and attack cost(AC). For each feasible node–method pair we compute these three metrics, extract the Pareto set, prune with ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document}-constraints, and select a preference-aware compromise with VIKOR. In a Docker-orchestrated testbed, this Pareto-first pipeline achieves about +50%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+50\%$$\end{document} higher attack gain and roughly -29%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-29\%$$\end{document} lower attack Impact and attack cost compared with random selection.

Social Network Privacy Protection Based on Differential Privacy Technology and Community Discovery Algorithm

The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.

Understanding User-Generated Content and Communities in Decentralized Web3 Networks: An Empirical Study on Nostr

Decentralized social protocols such as Nostr introduce a new paradigm for user-generated content (UGC) in the Web3 era, where content production, dissemination, and reward mechanisms operate without centralized governance. This paper presents one of the first large-scale empirical analyses of Nostr, based on 22.3 million user events collected from four major publicly accessible relays. Guided by three research questions, we examine (1) the temporal and spatial distribution of user participation, (2) the structural characteristics of decentralized UGC networks, and (3) thematic and incentive patterns in content creation and Zap-based rewards. Our analysis shows rapid growth followed by long-tail stabilization, while the interaction network remains highly modular and loosely connected, indicating fragmented yet persistent communities. Embedding-based clustering of textual posts identifies ten clusters on several topics: technical discussions, ideological debates, personal expression, community coordination, and media sharing, highlighting a hybrid ecosystem of social and technical discourse. We further find that knowledge-oriented content in Clusters 1 and 5 receives higher Zap engagement, suggesting the socialization of a primarily technical infrastructure. These findings advance the understanding of decentralized multimedia ecosystems by linking network decentralization with observed participation and engagement patterns in the absence of centralized moderation.