Privacy and Security in Decentralized and Anonymous Systems
Computational research on anonymity, trust, and misinformation risks in decentralized and low-accountability online systems.
Research at a Glance
| Topic | Data and Methods | Main Finding | Key Papers |
|---|---|---|---|
| Tor adoption and knowledge accumulation | Country-level panel data, search-trace modeling | Tor uptake is associated with motivation and how-to knowledge, with political-context differences in usage patterns. | (Chen et al., 2024) |
| Sustainability in anonymous communities | Dark Web and Surface Web forum traces, survival analysis, NLP | Anonymous forums show distinct interaction and sustainability dynamics; language-use patterns are associated with retention and exit. | (Chen & Liu, 2025; Chen et al., 2023) |
| Dark-side platform use and misinformation beliefs | US survey data, matching and IV robustness checks | Dark-side platform use is positively associated with misinformation beliefs in public-health and electoral contexts. | (Chen et al., 2025) |
Methods Stack
| Category | Methods |
|---|---|
| Inference | Panel models, survival analysis, matching and instrumental-variable designs |
| Computation | NLP, behavioral trace analytics, network analysis |
| Data | Cross-national traces, online community logs, survey datasets |
Program Direction
- Build reproducible measurement frameworks for online safety and platform risk.
- Publish methods and empirical findings across computing/security and interdisciplinary venues.
- Develop modular PhD projects in data engineering, modeling, and risk evaluation.