IoT device authentication must resist impersonation and credential theft while respecting the computational constraints of edge devices. Existing frameworks rely on static cryptographic keys that, once extracted, enable full impersonation, whereas RF fingerprinting schemes that bind identity to hardware imperfections transmit and store device templates in plaintext, exposing them to template theft and linkability attacks. ZK-RFAuth is a three-phase authentication framework that integrates Siamese neural network-based RF fingerprinting, Groth16 zero-knowledge proof (ZKP) verification, and proof-of-authority blockchain logging. During registration, a Siamese convolutional network extracts a compact embedding from raw I/Q samples and commits a Poseidon hash of the quantized mean template on-chain. During verification, the prover generates a Groth16 proof demonstrating that the L1 distance between a fresh embedding and the registered template falls below a per-device threshold without revealing either vector. The proof and authentication outcome are recorded on-chain for tamper-evident auditing. Evaluated on the WiSig dataset (28 WiFi transmitters, 224,000 frames), ZK-RFAuth achieves 91.4% closed-set accuracy and 2.25% equal error rate at embedding dimension d = 64, with 88.4% genuine acceptance rate and 70.8% open-set rogue rejection using per-device P95 thresholds. The ZKP circuit requires only 972 rank-1 constraint system (R1CS) constraints over 100× fewer than an equivalent SHA-256 circuit producing 144-byte proofs verifiable in approximately 3 ms.
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Wireless Signal Modulation Classification
Physical Unclonable Functions (PUFs) and Hardware Security
Shannon's rate-distortion theory treats source symbols as unstructured labels. When the source is a knowledge base equipped with a logical proof system, a natural fidelity criterion is closure fidelity: a reconstruction is acceptable if it preserves the deductive closure of the original. This paper develops a rate-distortion theory under this criterion. Central to the theory is the irredundant core-a canonical generating set extracted by a fixed-order deletion procedure, from which the full deductive closure can be rederived. We prove that the zero-distortion semantic rate equals a quantity that is strictly below the classical entropy rate whenever the knowledge base contains redundant states. More generally, the full semantic rate-distortion function depends only on the core; redundant states are invisible to both rate and distortion. We derive a semantic source-channel separation theorem showing a semantic leverage phenomenon: under closure fidelity, the required source rate is reduced by an asymptotic leverage factor greater than one, allowing the same knowledge base to be communicated with proportionally fewer channel uses-not by violating Shannon capacity, but because redundant states become free. We also prove a strengthened Fano inequality that exploits core structure. For heterogeneous multi-agent communication, an overlap decomposition gives necessary and sufficient conditions for closure-reliable transmission and identifies a semantic bottleneck in broadcast settings that persists even over noiseless channels. All results are verified on Datalog instances with up to 24,000 base facts.
Volker Ziegler, Peter Schneider, Harish Viswanathan, Michael Montag · 6 authors
A comprehensive set of security technology enablers will be critically required for communication systems for the 6G era of the 2030s. Trustworthiness must be assured across IoT, heterogenous cloud and networks, devices, sub-networks, and applications. The 6G threat vector will be defined by 6G architectural disaggregation, open interfaces and an environment with multiple stakeholders. Broadly decomposed into domains of cyber-resilience, privacy and trust and their respective intersection, we explore relevant security technology enablers including automated software creation and automated closed-loop security operation, privacy preserving technologies, hardware and cloud embedded anchors of trust, quantum-safe security, jamming protection and physical layer security as well as distributed ledger technologies. Artificial intelligence and machine learning (AI/ML) as a key technology enabler will be pervasive and of pivotal relevance across the security technology stack and architecture. A novel vision for a trustworthy Secure Telecom Operation Map is developed as part of the automated closed loop operations paradigm.