Artificial intelligence (AI) agents are increasingly capable of initiating financial transactions on behalf of users or other agents. This evolution introduces a fundamental challenge: verifying both the authenticity of an autonomous agent and the true intent behind its transactions in a decentralized, trustless environment. Traditional payment systems assume human authorization, but autonomous, agent-led payments remove that safeguard. This paper presents a blockchain-based framework that cryptographically authenticates and verifies the intent of every AI-initiated transaction. The proposed system leverages decentralized identity (DID) standards and verifiable credentials to establish agent identities, on-chain intent proofs to record user authorization, and zero-knowledge proofs (ZKPs) to preserve privacy while ensuring policy compliance. Additionally, secure execution environments (TEE-based attestations) guarantee the integrity of agent reasoning and execution. The hybrid on-chain/off-chain architecture provides an immutable audit trail linking user intent to payment outcome. Through qualitative analysis, the framework demonstrates strong resistance to impersonation, unauthorized transactions, and misalignment of intent. This work lays the foundation for secure, auditable, and intent-aware autonomous economic agents, enabling a future of verifiable trust and accountability in AI-driven financial ecosystems.
Prof. S. H. Thengil, Tanmay Sadanshiv, A. M. Patil, Shreyash Trimbake · 5 authors
Abstract - With the increasing volume of digital evidence in law-enforcement and judicial processes, ensuring integrity, traceability and tamper-resistance has become paramount. This paper presents the Blockchain Evidence Archive System (BEAS), a decentralized application that leverages blockchain technology, smart contracts and the InterPlanetary File System (IPFS) to provide a secure, immutable and transparent evidence- management platform. Evidence metadata is stored on an Ethereum-based blockchain while the associated large files (images, videos, documents) are stored on IPFS with their cryptographic hashes recorded on-chain. Role-based access control ensures only authorized users such as police officers and court officials can upload, verify or access evidence. We describe the system architecture, implementation details, security features and evaluate the performance of the system in terms of upload time, verification latency and resistance to tampering. The results demonstrate that BEAS significantly improves evidence integrity and auditability when compared to conventional centralized systems. We conclude with a discussion on future enhancements including biometric integration, mobile accessibility and enterprise-scale deployment. l Key Words: Blockchain Technology, IPFS, Digital EvidenceManagement, Decentralized Application, Smart Contracts, Ethereum Network, Cryptographic Hashing, Data Integrity, Tamper- Proof Storage, Role-Based Access Control, Chain of Custody, Evidence Verification, Immutable Ledger, Secure File Storage, Decentralized Architecture, Forensics Technology, Law Enforcement Data Security, Distributed Ledger Technology
Rui Shi, Huamin Feng, Chunjie Cao, Yang Yang · 6 authors
Anonymous credentials are an essential cryptography primitive to protect user privacy and provide fine-grained access control for proving ownership and rights of specific credentials. There are currently two roadmaps to designing anonymous credentials: one is signature credentials, which are constructed by signature with efficient protocols and non-interactive zero-knowledge proofs, and the other is functional credentials, which are transformed from predicate encryption schemes. However, none of the existing instances of anonymous credentials support$expressive$access policies expressed as conjunction, disjunction, or arbitrary Boolean formulas, which are particularly useful for cloud services. In this paper, we propose a new fast and expressive anonymous credential, called FEAC. It is constructed with the unique$dual$$randomness$$splitting$technique, which combines the most efficient anonymous key-policy attribute-based encryption (USENIX 24) and short randomizable signature (CT-RSA 18) to balance efficiency, expressiveness, and security, demonstrating a new way to instantiate anonymous credentials. Furthermore, our credential presentation protocol offloads most of the time-consuming computation to the cloud server (11 pairing) to reduce the computational burden on the user side (2 pairing). We propose formal definitions and formal security proofs of FEAC. We provide implementations and evaluate the performance of FEAC, comparing it to state-of-the-art work.
GOWTHAMANI R. GOWTHAMANI R., J. Granty Regina Elwin, Kavin A, Rahul A · 6 authors
Elections are the base for a democratic society, but there are problems like fraud tampering, slow result processing in the current voting systems. To solve these problems, this project introduces a Blockchain-Based Voting System as a scalable solution. This system uses IOTA's Tangle (a Directed Acyclic Graph model) to provide fast, scalable, and feeless transactions, making it suitable for large-scale voting. The personal details are kept secret by using Zero-Knowledge Proofs (ZKPs). A Hybrid Cloud Model is used, where sensitive data is stored securely on private servers, while the public network ensures transparency. Multi-Factor Authentication (MFA) adds an extra layer of security by blocking unauthorized access. By combining these technologies, the system makes voting tamper-proof, keeps voters anonymous, allows real-time result announcements, and protects against frauds. Overall, this solution shows how blockchain can improve the trust, fairness, and accessibility of democratic participation.
Blockchain technology into the healthcare sector, logistics, finance, and massive IoT system, this technology is making its way into it. With the issue of privacy becoming much more critical than before in the adoption phases. Even though blockchains provide integrity and resistant storage against tampering, numerous implementations still expose behavioural evidence, time-based data, besides other forms of metadata, which may be compromised by inference attacks. These breaches become more apparent when there is an exchange or near real-time processing of sensitive information. This paper will analyze the performance of three widely discussed privacy-saving methods Zero-Knowledge Proofs (ZKP), Homomorphic Encryption (HE), not to mention Differential Privacy (DP) in a practical blockchain architecture and not in isolation. A testbed made of Hyperledger Fabric and Ethereum instances privately owned was constructed to enable each of the methods to be tested under the same conditions. The experiments made trade-offs of transaction suspensions, added computational load, and exposure to metadata-based attacks.The results demonstrate that ZKP-based settings store observable metadata substantially and restricted the amount that an adversary would deduce regarding transaction patterns. HE was a good implementation in areas where safe computation was needed but with significant latency overhead. The least significant communication impact was on DP, which was, however, to be carefully tuned in terms of its privacy parameters to prevent the decline in protection. We further conducted simulated attacks to research into the reaction of each approach to pressure and detail how validation can be extended in the future with the aid of open datasets through the MIMIC-III, OpenAQ, and TII cybersecurity logs. On the whole, the findings provide useful insights to teams, who want to embed cryptographic privacy tools in production-scale blockchain systems in particular in those industries where confidentiality and data protection cannot be jeopardized.
Rajesh Sehgal, Anish Gupta, G. Premananthan, Ahmed Anwer Jaafa · 6 authors
Due to increased globalization and international travel, there is a growing demand for secure and private identity verification systems at border entries. Reliance on databases controlled by only a few hinders quick, safe, and easy border crossings for travelers. The paper proposes a system utilizing blockchain technology that would enable individuals to verify their identities in real-time when traveling internationally. The system suggested for travelers utilizes selfsovereign identity technology, allowing them to manage their verified IDs on their devices through wallets based on blockchain technology. Once biometric details, such as facial recognition, fingerprints, and iris scans, are collected from each individual, they are linked together on the identical federated, permissioned blockchain by the nation's and border agencies. With the help of smart contracts, it is possible to control entry such that only approved agencies verify traveler documents, and zero-knowledge proofs ensure that only essential attributes about a person's identity are disclosed. Simulations prove that the system reduces verification time to under two seconds, significantly outperforming traditional systems, and prevents nearly 90% more identity fraud. Additionally, since blockchain is decentralized and secure, it ensures that audit records are transparent and there are no single points of failure. The work introduces a manageable and compatible system that enables people to travel more efficiently while facilitating collaboration between border officials and countries. The next step is to do significant pilot deployments, connect to current travel technology, and face rules and differences among countries and regions to make sure air (/land) mobility grows for everyone.
With the advancement of the information age, the widespread application of electronic evidence in fields such as justice and finance has brought new challenges. Although existing blockchain electronic evidence sharing schemes have immutability and transparency, they still have shortcomings in access control, data privacy protection, and efficiency. In addition, traditional attribute encryption strategies lack effective revocation mechanisms and cannot fully protect privacy when implementing fine-grained access control. Therefore, in order to address the above limitations, a blockchain electronic evidence sharing scheme based on an improved ciphertext policy attribute encryption combined with zero knowledge proof technology has been proposed. The research innovatively introduces revocable ciphertext strategy encryption, which addresses the security risks caused by decryption key leakage through revocation function, ensuring the secure storage and sharing of electronic evidence. Meanwhile, the study also improved the PBFT consensus algorithm to enhance its performance in handling large volumes of transactions. The results showed that the storage TPS of the research model reached 492, and the query TPS reached 655. The computational cost of improving the PBFT consensus algorithm is 1.94 × 10 4 , and the maximum computational cost of the electronic evidence access control model based on zero knowledge proof is 509. Compared with traditional blockchain based electronic evidence sharing methods, the improved method not only enhances storage and sharing efficiency, but also further strengthens privacy protection capabilities by combining zero knowledge proof technology. In summary, the research method effectively achieves secure sharing and privacy protection of electronic evidence on blockchain, providing support and reference for electronic evidence storage in fields such as justice and finance. However, there are still challenges in terms of scalability and data storage in the research, so algorithms can be optimized in the future to further improve the application scope of the system.
This research introduces a post-quantum crypto-infrastructure exploiting the CRYSTALS-Kyber lattice based key encapsulation and the CRYSTALS-Dilithium digital signature cryptography supporting the real time payment transactions against quantum computing based attacks. Besides, based on the open quantum safe library OpenQuantumSafe and TensorFlow Federated, a hybrid classical-quantum federated key management protocol for payment systems is built. With the system, transaction validation can be checked without decryption based on HOMORPHIC encryption using Microsoft SEAL toolkit with sub-200ms latency. For anomaly detection application in neural network, LSTM auto-encoder can detect quantum based attacks with an accuracy of 97.4%. Implementation on AWS Braket Simulator for Verification against Shor algorithm Quantum Resistance and Grover algorithm. The model delivered Redis heavily boronated, for high speed key caching, and Apache Kafka, for asynchronous streaming of transactions at a speed of 50,000 transactions per second. Securely storing the cryptographic keys using an integration with HSM PKCS#11 hardware interface Blockchain anchoring with Hyperledger Fabric: GDPR compliance audit trail immutability with zero-knowledge-proofs (libsnark).
Internet of Things (IoT) facilitates intelligent interconnection and data exchange between devices. However, existing data aggregation schemes face challenges such as edge node disconnections, high computational overhead, and limited fault tolerance, which affect the reliability and efficiency of the system. To address these issues, this paper proposes a smart contract assisted and fault-tolerant data aggregation scheme without a trusted authority, named Cap. To realize it, we integrate blockchain technology and homomorphic encryption. Through smart contracts, we dynamically select and switch edge nodes to ensure that the data aggregation process continues even if some nodes go offline. Moreover, by leveraging homomorphic secret sharing, we effectively minimize communication overhead and ensure that users can exit the aggregation process without jeopardizing data integrity. This scheme provides an efficient and reliable solution for secure data aggregation in Iot environments and enhances the robustness and data security of the system. Performance evaluation show that, compared with existing schemes, Cap reduces computational and communication overhead by 30%.
C Ratna Prabha, K Mahesh Babu, Kattinti Dheeraj, N Varsha Priya · 5 authors
"Non-Fungible Tokens" (NFTs) have revolutionized digital ownership by enabling verifiable, unique assets on the blockchain. In this paper, we present an advanced, secure, and scalable "NFT marketplace" built on Ethereum, leveraging Zero-Knowledge Proofs (ZKPs), multi-signature wallets, and AI-driven threat detection to enhance security and decentralization. We implement ERC-721 and ERC-1155 standards, integrate Layer 2 solutions (Polygon zkEVM, Arbitrum) for cost efficiency, and introduce decentralized identity (DID) verification to prevent fraud. Our smart contracts were thoroughly tested using Slither, MythX, and manual audits, while the frontend employs React.js with WalletConnect v2 for cross-wallet compatibility. We also propose a novel royalty enforcement mechanism using ERC2981 with automated payouts via Chainlink Automation. Performance benchmarks demonstrate sub-second transaction finality on Layer 2 and gas cost reductions of up to 90% compared to traditional Ethereum mainnet deployments.
List decoding of codes can be seen as the generalization of unique decoding of codes while list decoding over finite fields has been extensively studied, extending these results to more general algebraic structures such as Galois rings remains an important challenge. Due to recent progress in zero knowledge systems, there is a growing demand to investigate the proximity gap of codes over Galois rings in Yizhou Yao(2025). The proximity gap is closely related to the decoding capability of codes. It was shown in Eli Ben-Sasson(2020) that the proximity gap for RS codes over finite field can be improved to $1-\sqrt{r}$ if one consider list decoding instead of unique decoding. However, we know very little about RS codes over Galois ring which might hinder the development of zero knowledge proof system for ring-based arithmetic circuit. In this work, we first extend the list decoding procedure of Guruswami and Sudan to Reed-Solomon codes over Galois rings, which shows that RS codes with rate $r$ can be list decoded up to radius $1-\sqrt{r}$. Then, we investigate the list decoding of folded Reed-Solomon codes over Galois rings. We show that the list decoding radius of folded Reed-Solomon codes can reach the Singlton bound as its counterpart over finite field. We also extend the deterministic pruning method of Vikrant Ashvinkumar(2026) to Galois rings, showing how to prune the affine free module obtained from the linear-algebraic decoder and recover the candidate codewords. Finally, we improve the list size of our folded Reed-Solomon code to $O(1/\varepsilon^2)$ by extending recent work in Shashank Srivastava(2025) to Galois Rings. By developing the recent work of Yeyuan Chen(2025), we show that folded Reed-Solomon codes over Galois rings satisfy the relaxed generalized Singleton bound in the average-radius sense with optimal list size $O(1/\varepsilon)$.
Zero-knowledge proof is one of the most promising privacy-preserving approaches in the current literature. However, its complex nature leads its development and deployment to be time-consuming and error-prone. Furthermore, despite the increasing adoption of zero-knowledge proof in the blockchain applications, there is still no systematic framework that streamlines the end-to-end life-cycle of proof development. This paper introduces the first LLM-enhanced zero-knowledge proof DevOps framework for blockchain (i.e. zkOps) to the literature. To evaluate the performance of the framework on different real-life scenarios, a small benchmarking suite is constructed with the increasing computational complexity with respect to the size of circuit constraints. The experimental study identifies the effects of the model temperature on the code compilation rate, and the complexity of prompts on the service latencies. The findings show that the framework efficiently handles the varying-complexity of prompts with a maximum successful compilation rate of 70% (i.e., up to 200,000 proof constraints).
Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.
Abstract In today’s era of digital transformation, online transactions have become vital to financial systems, e-commerce, and decentralized applications. However, increasing dependence on digital payment infrastructures has also raised major security concerns such as hacking, identity theft, and unauthorized access. To address these challenges, the proposed project “Blockchain Secure Transaction” presents a decentralized framework that ensures transparency, integrity, and confidentiality in digital transactions. The system uses blockchain technology to record and validate each transaction in a distributed ledger, eliminating centralized control and making data immutable and tamper-proof. The workflow begins with user registration, where users provide details and set a picture password for secure recognition. During login, the system verifies credentials and performs biometric authentication to confirm user identity. Unregistered users are redirected to the registration page, maintaining process integrity. Once authenticated, users access the dashboard to initiate secure transactions. To preserve privacy, Zero-Knowledge Proof (ZKP) is used, allowing users to prove transaction authenticity without revealing sensitive information. Transactions then pass through smart contract verification, which ensures compliance with predefined conditions. Successful verifications result in completed transactions, while suspicious or invalid ones are blocked or frozen automatically. All user data and transaction logs are securely stored in Firebase, with backend processing handled in Java and the frontend designed using React (app.jsx). By combining blockchain’s immutability, smart contract automation, ZKP privacy proofs, and biometric authentication, the Blockchain Secure Transaction System offers a multi-layered, tamper-resistant, and transparent solution for secure online payments — enhancing trust and reliability in the digital economy.
Privacy in blockchain-based cryptocurrencies has become a critical area of research and development. This is driven by the transparent nature of public ledgers and the growing demand for confidential transactions. This study provides a comprehensive exposition of a prominent privacyenhancing protocol, Zcash. We begin by elucidating the design and operation of Tornado Cash, a smart contract-based mixer on Ethereum that leverages zero-knowledge proofs to enable unlinkable transactions. Building on this foundation, we explore the Zcash protocol, tracking its evolution from Bitcoin and its innovative use of zk-SNARKs. We also examine the intricate mechanisms that highlight its privacy guarantees, including circuit design, Merkle tree structures and transaction relations. By systematically analyzing and comparing these protocols, this study aims to clarify their inner workings, highlight their strengths and limitations, and provide information on the future of privacy in decentralized finance.
This research introduces a next-generation cryptographic framework aimed at securing payment systems and IoT-enabled financial ecosystems against both contemporary cyber threats and the anticipated risks posed by quantum computing. The proposed architecture supports intelligent payment cards, contactless transactions, and IoT-based banking infrastructures by integrating classical cryptographic methods—such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC), Secure Hash Algorithm 3 (SHA-3), and Hash-based Message Authentication Code (HMAC)—with post-quantum algorithms, including CRYSTALS-Kyber and Dilithium. In addition, the framework aligns with widely adopted industry standards, such as the Payment Card Industry Data Security Standard (PCI DSS), Europay-MasterCard-Visa (EMV), ISO 27001, and National Institute of Standards and Technology (NIST) guidelines, thereby ensuring compliance and regulatory resilience. To address the evolving cyber threat landscape, the system integrates blockchain-based decentralized identity management with zero-knowledge proofs (ZKPs) for trustless authentication. Furthermore, privacy-preserving techniques such as Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE) are employed to enable secure processing of encrypted data. The security framework is further strengthened through AI-driven fraud detection, which leverages deep learning and federated learning models to detect anomalies in real time without compromising customer privacy. Additionally, confidential computing enclaves and hardware trust anchors—including Hardware Security Modules (HSMs), Trusted Platform Modules (TPMs), and Physically Unclonable Functions (PUFs)—are utilized to enhance system integrity and reliability. By combining quantum-resistant cryptography, privacy-preserving computation, and intelligent anomaly detection, this work presents an adaptive and future-proof security roadmap for the financial sector. The proposed framework is designed not only to counteract today’s sophisticated cyberattacks but also to maintain resilience against the emerging challenges of the quantum era.
Physical Unclonable Functions (PUFs) and Hardware Security
Blockchain technology is evolving rapidly, bringing significant opportunities as well as complex challenges, particularly in the area of smart contract security This paper addresses these challenges by presenting an enhanced version of Slither, a static analysis framework, specifically designed to improve vulnerability detection in Ethereum-based smart contracts. Our contributions target critical vulnerabilities of reentrancy, unchecked call return values, and access control issues, which pose serious threat to decentralized apps (dApps). The enhanced Slither tool incorporates advanced detection methodologies, including refined control-flow analysis, sophisticated pattern recognition, and comprehensive visibility checks. Experimental evaluation demonstrates that this proposed tool significantly improves accuracy, precision, recall, and F1-score across various metrics, while also effectively reducing false positives. This advanced model not only strengthens the security posture of smart contracts but also contributes to fostering a more secure and reliable blockchain ecosystem.
Blockchain technology offers a mechanism for storing data with cryptographic links between blocks, creating a tamper-resistant ledger. Although this immutability ensures data integrity, it complicates recovery in cases of errors or intrusions. This work proposes Bonsai, an error and intrusion recovery system designed for token exchanges on Ethereum based applications. The system includes a custom ERC-20 token (BON) that maintains a one to one peg with ETH tokens while enabling transaction reversals through arbitration trials and an insurance mechanism to protect users against losses. Our experimental evaluation on Ethereum Sepolia and ZKsync Sepolia demonstrates that Bonsai can successfully trace and reverse token flows through up to five wallets in under 20 seconds, at an average cost of approximately ${\$}$0.30 on ZKsync. Existing blockchain recovery approaches are slow and costly, with reversal operations taking up to 126 seconds, and some may not be able to complete the reversal. The system provides a practical solution for blockchain applications requiring error and intrusion correction capabilities while preserving the authentication, integrity, immutability, and non-repudiation properties of Blockchain.
Kassem Danach, Abbas Tarhini, Wael Hosny Fouad Aly, Hussin Hejase
Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.