Blockchain Papers

The Hamiltonian cycle problem is a well-known NP-complete problem in graph theory. This problem relates to lots of practical problems such as designing very large scale integration (VLSI) and travel-ling salesman problem (TSP). Since it is NP-complete, there is no efficient algorithm to solve the Hamiltonian cycle problem, and hence, its solution is valuable. In this paper, we propose new physical zero-knowledge proof protocols for the Hamiltonian cycle problem, whereby an entity can prove its knowledge of a solution to another entity without leaking any information about the valuable solution. Our protocols are more efficient than the previous protocols. We also propose a physical zero-knowledge proof protocol for TSP, one of whose building blocks is a new representation of an integer commitment with a secure addition protocol.

Open access

Formal Methods in Verification

Spacecraft Dynamics and Control

Vehicle Routing Optimization Methods

Original source

Jan 1, 2026·SSRN Electronic Journal

0 cites

Constitutional Logic in the Ethereum Virtual Machine: A Technical Implementation Report on Ternary Moral Logic

Lev Goukassian

This technical report presents the reference implementation of Ternary Moral Logic (TML) within the Ethereum Virtual Machine (EVM) ecosystem. It addresses the limitations of traditional "Code is Law" architectures by introducing a finite state machine that enforces a mandatory third state—the "Sacred Zero" or Epistemic Hold—allowing smart contracts to pause execution when pre-defined ethical conditions are unmet. The report moves beyond theoretical ethics to specify the Solidity design patterns, storage layouts, and cryptographic verification methods required to make TML enforcement non-bypassable and auditable. Key Technical Contributions: Finite State Machine (FSM): Implements a mandatory "Sacred Zero" state (State 0) that acts as an "Epistemic Hold," distinguishing between valid (1), invalid (-1), and uncertain (0) transaction states. Dual-Lane Latency Architecture: Defines a "Fast Lane" for synchronous, clear-cut transactions and a "Slow Lane" for ambiguous cases requiring governance or oracle resolution, preventing head-of-line blocking. Cryptographic Provenance: Utilizes EIP-712 typed data signing to bind off-chain AI/Oracle verdicts to on-chain execution, preventing replay attacks and ensuring distinct domain separation. Privacy Preservation: Integrates Zero-Knowledge Proofs (ZK-SNARKS) to verify the execution of moral logic models without revealing sensitive input data or proprietary model weights ("Glass Box" architecture). Immutable Core Pattern: Rejects standard upgradeable proxy patterns in favor of an "Immutable Core" architecture to eliminate administrative "God Mode" and ensure constitutional constraints cannot be bypassed by key holders. Formal Verification: Demonstrates safety and liveness properties (e.g., "No Silent Pause," "Eventual Resolution") using TLA+ (Temporal Logic of Actions) to mathematically prove the system's robustness.

Open access

2 source records

Blockchain Technology Applications and Security

Scientific Computing and Data Management

Multi-Agent Systems and Negotiation

Original source

Jan 1, 2026·International Journal of Intelligent Systems

1 cites

Quantum‐Enhanced Zero‐Knowledge Compression Used for Cloud IoT Healthcare: A Scalable, Privacy‐Preserving QZ‐HCN Framework

Rajasekaran P., Duraipandian M., Johny Renoald Albert, R. Jamuna · 5 authors

The Internet of Medical Things (IoMT) in the IoT with Cloud Healthcare (CHI) creates a high volume of real‐time medical data, but traditional compression methods suffer high computation costs, privacy leaks and quantum attacks, while advanced cryptographic algorithms such as homomorphic encryption are costly and have poor scalability for the real‐time system application. In this work, we propose a quantum‐enhanced zero‐knowledge healthcare compression network (QZ‐HCN) that associates zero‐knowledge proofs (ZKPs) with quantum‐inspired deep learning (QIDL) by introducing an innovative adaptive quantum‐supported ZKP verification mechanism (AQ‐ZKV) and a quantum fusion autoconventional neural network (QF‐AutoCNN) technique to achieve efficient, privacy‐preserving compression. For healthcare IoT datasets, QZ‐HCN can reach 98.16% in accuracy, 97.09% in F‐measure, 96.32% in precision and 97.45% in recall, with a throughput of 449.57 bits/s; processing time is reduced to 0.85 s, and memory cost is minimised to be only 192 kbits, which outperforms CNN‐Encryption (90.23% accuracy), proxy re‐encryption and homomorphic encryption by at most 13 percentage points in accuracy and 75 percentage points in memory efficiency. The secure and scalable management for CHI data is achieved by QZ‐HCN, which solves the problems of privacy threats and space costs of real‐time medical applications.

Open access

Cryptography and Data Security

Cryptographic Implementations and Security

Chaos-based Image/Signal Encryption

Original source

Jan 1, 2026·IEEE Transactions on Network Science and Engineering

1 cites

Security and Verifiability in Federated Learning: A Zero-Knowledge Reputation-Based Blockchain Framework

Swetha Ghanta, Ashok Kumar Pradhan, Prasanthi Boyapati, Sujit Biswas · 5 authors

Federated Learning (FL) enables collaborative training without centralizing sensitive data but faces challenges, including client authenticity, verifiable training participation, and secure aggregation. To overcome these challenges, we propose a novel framework, Zero-Knowledge Reputation-aware Blockchain Federated Learning (ZK-RBFL), which integrates blockchain, FL, Homomorphic Encryption (HE), and zero-knowledge proofs (ZKP). In the proposed ZK-RBFL framework, initially the clients undergo lightweight token-based authentication and then generate ZKP to provide cryptographic evidence of honest local training participation and reported inference accuracy before contributing their model updates. The model updates are encrypted using the CKKS HE mechanism to prevent any potential model inversion attacks. These encrypted model updates are stored on IPFS, with their corresponding CIDs recorded on the blockchain to ensure immutability. Further, ZK-RBFL enables mutual client verification of ZKPs to reduce server bottlenecks and enhance accountability. To ensure fairness and robustness in a distributed environment, we introduce a democratic blockchain consensus mechanism named Proof of Reputation-Weighted Voting (PoRWV) for block acceptance. Once consensus is reached, the encrypted model updates are aggregated using reputation-weighted averaging. We demonstrate the effectiveness of ZK-RBFL for brain tumor classification using a ZKP-compatible LeNet model for proof generation. Despite model simplicity, the global model achieves 94.22% accuracy. In addition, experiments with malicious clients and formal Scyther security analysis demonstrate that ZK-RBFL ensures both security and performance.

Privacy-Preserving Technologies in Data

Cryptography and Data Security

Blockchain Technology Applications and Security

Original source

Jan 1, 2026·arXiv (Cornell University)

0 cites

Automating Bitvector and Finite Field Equivalence Proofs in Lean

Elizaveta Pertseva, Valentin Robert, Clark Barrett, James Parker

Efforts to verify Zero-Knowledge Proof circuit encodings have highlighted the challenge of proving the correctness of quantifier-free statements that make use of both bitvector and finite field operations. Existing verification workflows are either manual or rely on SMT solvers, which scale poorly on some classes of problems for reasons that include difficulties with conversion operators and challenges reasoning about inequalities. To address these limitations, we present a novel Lean tactic BitModEq that leverages range lemmas and case analysis to produce verified translations from finite fields to bitvectors. Our approach, combined with bit-blasting, outperforms state-of-the-art SMT solvers, solving 19% more ZKP arithmetization benchmarks.

Open access

4 source records

cs.LO

Formal Methods in Verification

Physical Unclonable Functions (PUFs) and Hardware Security

Original source

Jan 1, 2026·2026 IEEE SoutheastCon, Huntsville, AL, USA, 2026

0 cites

Device-Native Autonomous Agents for Privacy-Preserving Negotiations

Joyjit Roy, Samaresh Kumar Singh

Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.

Open access

4 source records

cs.CR

cs.AI

cs.ET

Original source

Jan 1, 2026·Net ZERO

0 cites

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Original source