Proposal: Privacy Analysis Tool + Future ZK Extensions for py-libp2p

[Hany-Almnaem]

Hi everyone 👋

I'd like to propose a privacy analysis tool for py-libp2p with a clear phased approach:

🎯 Phase 1: Privacy Analysis MVP (2-3 months)

Goal: Help developers identify privacy/anonymity leaks in their libp2p applications.

What it would detect:

• Peer ID exposure patterns
• Timing correlation vulnerabilities
  -Traffic volume fingerprinting
• Session linkability risks
• Transport-specific metadata leakage

Proposed Implementation:

• Standalone Python package (for modularity)
• Hooks into py-libp2p node instrumentation
• CLI tool + programmatic API
• Clear privacy reports with actionable recommendations

Proposed Milestones:

1. Design & Scope (Week 1-2): Define measurement targets, Python integration points
2. Instrumentation (Week 3-4): Hook into py-libp2p to log metadata (peer IDs, timestamps, multiaddrs)
3. Analysis & Visualization (Week 5-6): Build privacy reports, connection graphs, timing histograms
4. Tool Release (Week 7-8): Publish findings + tool as standalone package

API sketch:

from libp2p_privacy import PrivacyAnalyzer

analyzer = PrivacyAnalyzer(libp2p_node)
report = analyzer.analyze()
print(report.summary()) # Shows risk levels + recommendations

🚀 Phase 2: ZK-Enhanced Privacy (Future Vision)

Building on Phase 1 findings, explore zero-knowledge proofs for cryptographic privacy guarantees:

• Anonymity set proofs: "I'm one of N peers" without revealing which
• Unlinkability proofs: Cryptographically prove sessions can't be related
• Traffic indistinguishability: ZK proofs that timing/patterns don't leak identity

Python's ZK ecosystem (circom-python, PySnark) makes this feasible for future exploration.

🤔 Questions for the Community

1. Scope: Does Phase 1 align with py-libp2p's priorities?
2. Integration: Start standing alone or build tighter integration from the beginning?
3. APIs: What privacy metrics would be most valuable to developers?
4. ZK Interest: Anyone interested in exploring the Phase 2 vision?

Why This Matters

• Gives developers concrete privacy diagnostics for their apps
• Attracts privacy-critical use cases (healthcare, finance, activism)
• Positions py-libp2p as a leader in privacy-preserving P2P
• Opens path toward privacy certification for distributed systems

Very open to feedback and collaboration! Would love to hear thoughts from maintainers and contributors 🙌

[seetadev]

@Hany-Almnaem : Thank you so much for sharing the discussion proposal. Appreciate it. Reviewing it in details.

Wish to also CC contributors in Py-libp2p, who have expressed interest to pursue certain aspects of this initiative:

CC @Fatumayattani, @Nkovaturient and @acul71

[Hany-Almnaem]

Thanks @seetadev! Really appreciate you taking the time to review it in detail.

[Hany-Almnaem]

Excited to hear from @Fatumayattani, @Nkovaturient, and @acul71 as well - would love to hear your perspectives on the proposal and any areas you'd be particularly interested in exploring.
Looking forward to the feedback and figuring out the best path forward together! 🙌

[Fatumayattani]

I’m interested in contributing to the instrumentation and metrics part, especially how the analyzer can hook into py-libp2p to log and process metadata like peer IDs, timestamps, and multiaddrs. There’s also a strong connection with the network attack simulation framework we’ve been building since the same setup can be extended to capture privacy-related signals like metadata exposure or timing correlations. Linking both efforts can give a more complete picture of network security and privacy in py-libp2p.

[Fatumayattani]

This is a well-structured proposal, @Hany-Almnaem . The focus on practical privacy analysis and the way you’ve structured the phases feels very clear and purposeful. I’m interested in contributing and look forward to being part of the work as it moves ahead.

[Hany-Almnaem]

Hi @Fatumayattani! Thanks so much for the interest and kind words 🙏 Really excited to have you involved! Once we get some feedback from the maintainers on scope and priorities, I'd love to coordinate on how we can work effectively. In the meantime, feel free to share any thoughts. Looking forward to collaborating! 🚀

[Hany-Almnaem]

🎉 Phase 1 Complete: Privacy Analysis Tool is Working with Real py-libp2p!

Hi everyone! 👋

I'm excited to share that Phase 1 of the Privacy Analysis Tool is complete and fully integrated with real py-libp2p networks!

🔗 GitHub Repository

https://github.com/Hany-Almnaem/libp2p-privacy-poc

✅ What's Working

Real py-libp2p Integration:

• ✅ Full integration using INotifee interface for automatic event capture
• ✅ Works with real TCP connections, not simulations
• ✅ Validated with background_trio_service() lifecycle management
• ✅ Multi-node network analysis (tested with 3+ node topologies)

Privacy Detection (6 Algorithms):

• Peer linkability analysis
• Timing correlation detection
• Small anonymity set identification
• Protocol fingerprinting
• Session tracking
• Connection pattern analysis

Deliverables:

• 📦 Standalone Python package (pip install -e .)
• 🖥️ CLI tool (libp2p-privacy analyze)
• 📊 Multiple report formats (console/JSON/HTML)
• 🧪 Comprehensive test suite (unit + integration + edge cases)
• 📝 Complete documentation
• 💡 Mock ZK proof system (4 proof types for demonstration)

🚀 Quick Demo

Find it here

The best way to see it in action:

git clone https://github.com/Hany-Almnaem/libp2p-privacy-poc
cd libp2p-privacy-poc
pip install -e .

Run basic 2-node analysis with real connections

python examples/basic_analysis.py

Run multi-node star network demo

python examples/multi_node_scenario.pyYou'll see real [PrivacyNotifee] Connected: messages showing live py-libp2p connections being captured and analyzed!

📊 Project Status

Phase 1 (Complete): Privacy Analysis MVP ✅

• ~3,500+ lines of code
• 6 privacy detection algorithms
• Real network integration throughout
• 18+ comprehensive tests passing
• Production-ready examples

Phase 1.5 (Complete): Real Network Validation ✅

• All examples converted to real connections
• Edge case testing (failures, reconnections, timeouts)
• CLI integration with real py-libp2p
• Performance validation

Phase 2 (Next): Real ZK Integration 🎯

• Replace mock ZK with real cryptographic proofs (PySnark2/Groth16)
• Implement trusted setup
• Add real privacy guarantees

🤝 Looking for Feedback & Collaboration

Special thanks to @seetadev for the support and @Fatumayattani, @Nkovaturient, and @acul71 for expressing interest!

@Fatumayattani: The instrumentation and metrics integration you mentioned is now implemented! The MetadataCollector hooks into py-libp2p via INotifee and captures peer IDs, timestamps, multiaddrs, and more. Would love your thoughts on how this could complement the network attack simulation framework you're building.

📖 Documentation

• README: Quick start & overview
• Complete Documentation: Detailed guide
• Known Issues: Current limitations

🎬 Next Steps

1. Plan Phase 2 real ZK integration approach
2. Consider production hardening path
3. Explore potential integration with py-libp2p core

Would love to hear your thoughts, feedback, and suggestions! 🙌

The tool is ready for testing and evaluation. Feel free to try it out and open issues/discussions on the repo!

[Fatumayattani]

Hi @Hany-Almnaem , this is really impressive work. The level of detail, the documentation, the reports and the overall polish make the tool feel very thoughtful and complete. I am testing it now and I am happy to start tackling the available issues as I go through everything. The instrumentation and metrics part is especially exciting since it fits so well with the simulation work. Looking forward to pushing this forward together.