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Paper 1

Proactive and privacy-Preserving defense for DNS over HTTPS via federated AI attestation (PAFA-DoH).

Basharat Ali, Guihai Chen

Year

2026

Journal

Neural networks : the official journal of the International Neural Network Society

DOI

10.1016/j.neunet.2025.108343

arXiv

-

DNS over HTTPS(DoH) improves privacy but still admits tunneling, resolver manipulation, and side-channel leakage. We present PAFA-DoH, a practical defense that unifies Federated AI, Quantum-resilient cryptography, and neuromorphic anomaly detection in a single, privacy-preserving framework. The system learns from encrypted traffic via adversarial federated learning with homomorphic inference, so models improve collaboratively without exposing raw queries. To surface hard-to-see behaviors(e.g., periodic C2 beacons), we extract topological signature from flows using persistence-diagram-based topological data analysis. Real-time detection is executed on Spiking Neural Network (SNN), running on neuromorphic hardware, delivering high throughput at low energy cost. Trust is enforced with an AI-assisted zero-knowledge attestation scheme that integrates ZK-SNARKs/STARKs to continuously verify client and resolver integrity without revealing metadata. We evaluate PAFA-DoH on a custom testbed that includes quantum-adversarial traffic, compromised-client simulations, and emulated rogue resolvers. Our Model achieves  ≥ 99.20 % malicious-activity detection accuracy,  < 100ms ZK verification, and  < 150 ms added handshake latency, while federated models converge within 10 training epochs. By combining privacy-preserving learning, verifiable trust, and event-driven analytics PAFA-DoH offers deployable path to zero-trust, post quantum-hardened protection for encrypted DNS.

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Paper 2

Tradeoffs on the volume of fault-tolerant circuits

Anirudh Krishna, Gilles Zémor

Year

2025

Journal

arXiv preprint

DOI

arXiv:2510.03057

arXiv

2510.03057

Dating back to the seminal work of von Neumann [von Neumann, Automata Studies, 1956], it is known that error correcting codes can overcome faulty circuit components to enable robust computation. Choosing an appropriate code is non-trivial as it must balance several requirements. Increasing the rate of the code reduces the relative number of redundant bits used in the fault-tolerant circuit, while increasing the distance of the code ensures robustness against faults. If the rate and distance were the only concerns, we could use asymptotically optimal codes as is done in communication settings. However, choosing a code for computation is challenging due to an additional requirement: The code needs to facilitate accessibility of encoded information to enable computation on encoded data. This seems to conflict with having large rate and distance. We prove that this is indeed the case, namely that a code family cannot simultaneously have constant rate, growing distance and short-depth gadgets to perform encoded CNOT gates. As a consequence, achieving good rate and distance may necessarily entail accepting very deep circuits, an undesirable trade-off in certain architectures and applications.

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