The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference, gradient inversion) and (2) trust in the aggregator, a single point of failure that can drop, alter, or inject contributions undetected. We present zkFL-Health, an architecture that combines FL with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs) to deliver privacy-preserving, verifiably correct collaborative training for medical AI. Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof (via Halo2/Nova) that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host. Verifier nodes validate the proof and record cryptographic commitments on-chain, providing an immutable audit trail and removing the need to trust any single party. We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost. This framework enables multi-institutional medical AI with strong confidentiality, integrity, and auditability, key properties for clinical adoption and regulatory compliance.
Blockchain technology is a game-changing invention that guarantees digital transactions on decentralized networks. The vital role that cryptography plays in guaranteeing the authenticity, confidentiality, and integrity of blockchains is examined in this paper. To secure the data on the blockchain and validate transactions, we are examining fundamental cryptographic techniques like hashing, symmetric and asymmetric encryption, and digital signatures. Furthermore, advanced cryptographic solutions that have the potential to improve privacy and scalability—such as homomorphic encryption, zero-knowledge proofs, and zk-SNARKs—are being discussed. Along with reviewing consensus techniques like proof of work and proof of stake, the paper contrasts the main blockchains, including those that are still in development, like Ethereum, Solana, and Hyperledger Fabric. Through an analysis of the advantages and disadvantages of existing cryptographic implementations, the study emphasizes the necessity for additional innovation.
The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
The Local Energy Market (LEM) is a key element in the energy sector's transition toward a decentralized system, enabling the integration of a growing number of small generation sources and energy storage facilities located at end-user locations.By utilizing digital energy trading platforms provided by LEMs, small consumers, producers, and prosumers actively participate in system balancing, which, among others, allows them to increase profits and energy independence.The efficiency of energy exchange in LEM is achieved by means of optimization methods that make use of sensitive participant data, such as energy consumption profiles.Therefore, ensuring privacy while simultaneously ensuring trust in the achieved optimal quantitative and qualitative results is crucial.The classic technology used in decentralized systems, i.e., blockchain, does not provide adequate scalability when transactions result from solving optimization problems.In this article, we analyze the possibilities of verifying optimization results by the use of cryptographic zero-knowledge proofs (ZKP).We explain how ZKP can support privacy and enable verification of computations without the need of repeating them for every participant.We also refer to existing ZKP implementations on LEM, while highlighting the barriers of high computational costs that prevent direct implementation of complex optimization algorithms within ZKP protocols.'To overcome these barriers, we present an approach integrating ZKP with optimality certificates, which has significant potential to increase the efficiency of
With the rapid proliferation of smart home cameras, wearable vision devices, and user-generated Consumer Internet of Things (CIoT) content, ensuring visual media authenticity, rightful ownership, and tamper detection has become increasingly challenging. We propose Chain-Visage, a blockchain-assisted framework for secure content authentication and tamper tracing in decentralized CIoT multimedia ecosystems. The termChainreflects the consortium blockchain backbone that guarantees immutable provenance, decentralized ownership management, and copyright revocation, whileVisagesymbolizes the unique visual identity of multimedia content achieved through dual-stage visual hash embedding. The proposed framework integrates Zero-Knowledge Proofs (ZKPs) for privacy-preserving ownership verification and employs optimized smart contracts to manage visual rights, provenance records, and ownership transfers efficiently. Evaluations on a large-scale dataset of over 10,000 real-world images and 3,850 video clips from diverse CIoT devices demonstrate Chain-Visage’s superior performance, achieving 97.5% traceability accuracy, 93% tamper detection sensitivity, and low verification latency even under resource-constrained environments. This work addresses a critical research gap in secure, privacy-preserving, and energy-efficient multimedia ownership control and tamper-resilient content authentication for next-generation CIoT ecosystems.
Open access
Blockchain Technology Applications and Security
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Aaron Chan, Alex Ding, Frank Sicong Chen, Alan Wu · 6 authors
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the Verifiability Trilemma, which posits that a decentralized inference system cannot simultaneously achieve high computational integrity, low latency, and low cost. Existing cryptographic solutions, such as Zero-Knowledge Machine Learning (ZKML), suffer from superlinear proving overheads (O(k NlogN)) that render them infeasible for billionparameter models. Conversely, optimistic approaches (opML) impose prohibitive dispute windows, preventing real-time interactivity, while recent "Proof of Quality" (PoQ) paradigms sacrifice cryptographic integrity for subjective semantic evaluation, leaving networks vulnerable to model downgrade attacks and reward hacking. In this paper, we introduce Optimistic TEE-Rollups (OTR), a hybrid verification protocol that harmonizes these constraints. OTR leverages NVIDIA H100 Confidential Computing Trusted Execution Environments (TEEs) to provide sub-second Provisional Finality, underpinned by an optimistic fraud-proof mechanism and stochastic Zero-Knowledge spot-checks to mitigate hardware side-channel risks. We formally define Proof of Efficient Attribution (PoEA), a consensus mechanism that cryptographically binds execution traces to hardware attestations, thereby guaranteeing model authenticity. Extensive simulations demonstrate that OTR achieves 99% of the throughput of centralized baselines with a marginal cost overhead of $0.07 per query, maintaining Byzantine fault tolerance against rational adversaries even in the presence of transient hardware vulnerabilities.
Zero-knowledge proof (ZKP) is a cryptographic protocol that allows a prover to convince verifiers that a computation is correctly executed without disclosing the prover’s secret. ZKP has been deployed in various privacy-preserving applications. However, the proof generation is notably inefficient on general-purpose processors. Multi-scalar multiplication (MSM) and polynomial computation (POLY), including number theoretic transform (NTT), are two of the most computation-intensive parts in proof generation. Recently, separate accelerators for MSM and POLY (mostly NTT) have been proposed. Unfortunately, separate accelerators may have poor resource utilization since MSM and POLY cannot be performed concurrently. To address this challenge, we propose Exa, a unified hardware architecture for MSM and POLY. It enables MSM and POLY to share computational resources and memory resources through decoupling dataflow control, computation, and memory. We design a novel unified functional unit (FU) array that can support both POLY operation and point addition (PADD) for MSM. In addition, we propose a 3-D NTT implementation and an adaptive MSM implementation on the FU array using a domain-specific instruction set architecture (ISA). Exa is scalable and can be efficiently orchestrated by our proposed runtime system. Compared with the separate accelerators for MSM and NTT, Exa occupies 47% less chip area. Compared to state-of-the-art accelerator PipeZK, Exa achieves up to$20.68 \times $and$4.58 \times $improvement for NTT and MSM, respectively, while occupying a chip area that is$2.6 \times $smaller. For end-to-end applications, Exa can achieve a speedup of$6.5 \times $on average than software implementation.
In this paper we present BlockMedLedger, a decentralized patient health record management system based on blockchain and IPFS. BlockMedLedger provides solutions to the challenges of healthcare data silos, security vulnerabilities and patient ownership of their own data. The patient centric model supports patients, medical data owners, to have complete control over their own medical data, while providing an efficient process to facilitate secure sharing of the medical data with care providers initiated through smart contracts and cryptographic access controls. The system uses an Ethereum compatible blockchain to support access control decision and IPFS for decentralized encrypted storage of encrypted medical records. The implementation demonstrates good security, efficient access, retrieval and sharing of encrypted health information for health care providers and patients while meeting requirements specified in HIPAA utilizing zero-knowledge proofs and patient consent control features.
This paper introduces an AI based Privacy Broker (AIBPB), a unified framework that combines blockchain, zero knowledge proofs, attribute based encryption, and differential privacy to enable verifiable and compliant data sharing in regulated environments. The system automatically interprets high level policies such as GDPR, HIPAA, and CCPA, and synthesizes optimized proof strategies through a multi objective cost privacy model. The architecture blends off chain cryptographic computation with on chain verification to balance privacy and transparency. Simulation based experiments demonstrate a 3.2 times improvement in proof generation latency, a 7.8 times reduction in information disclosure, and regulatory satisfaction rates exceeding 95% compared to baseline approaches. The results show that AI driven proof orchestration can significantly enhance scalability, compliance automation, and privacy protection in blockchain based systems.
Online harassment, incitement to violence, racist behavior, and other harmful content on social media can damage social harmony and even break the law. Traditional blocklisting technologies can block malicious users, but this comes at the expense of identity privacy. The anonymous blocklisting has emerged as an effective mechanism to restrict the abuse of freedom of speech while protecting user identity privacy. However, the state-of-the-art anonymous blocklisting schemes suffer from either poor dynamism or low efficiency. In this paper, we propose $\mathsf{ShadowBlock}$, an efficient dynamic anonymous blocklisting scheme. Specifically, we utilize the pseudorandom function and cryptographic accumulator to construct the public blocklisting, enabling users to prove they are not on the blocklisting in an anonymous manner. To improve verification efficiency, we design an aggregation zero-knowledge proof mechanism that converts multiple verification operations into a single one. In addition, we leverage the accumulator's property to achieve efficient updates of the blocklisting, i.e., the original proof can be reused with minimal updates rather than regenerating the entire proof. Experiments show that $\mathsf{ShadowBlock}$ has better dynamics and efficiency than the existing schemes. Finally, the discussion on applications indicates that $\mathsf{ShadowBlock}$ also holds significant value and has broad prospects in emerging fields such as cross-chain identity management.
Nadia van Niekerk, Brink van der Merwe, Louwrens Labuschagne
In the rapidly evolving realm of blockchain technology, the pursuit of enhanced privacy, security, and scalability has propelled the exploration of cryptographic innovations. Zero-Knowledge Proofs (ZKPs) have emerged as a pivotal solution, addressing diverse challenges across decentralized applications and cryptographic systems. However, the intricate mathematical foundations of ZKPs can pose a barrier to widespread adoption. To bridge this gap, a spectrum of ZKP tools has been developed, abstracting mathematical complexities and enabling developers with varying levels of expertise to incorporate ZKPs into their projects. The exploration of the 5 W’s – Who, What, When, Where, and Why – guides developers in selecting ZKP tools aligned with their specific needs and understanding. This paper serves as a vital resource for developers entering the dynamic landscape of ZKP development. By answering crucial questions and providing nuanced insights into ZKP tools, it empowers developers to navigate this intricate domain effectively. As ZKP technology continues to evolve, our findings contribute to the ongoing dialogue surrounding its implementation, utilization and the ever-adapting toolkit shaping the future of cryptographic innovation. This paper employs a Mining Software Repositories (MSR) approach to unravel insights from the expansive landscape of ZKP development. By delving into GitHub repositories, we categorize author archetypes, discuss ZKP proof constructions, identify phases of tool development, explore the level of understanding required and examine the correlation between tool types and application purposes. Through a metrics-driven analysis, we unveil patterns in tool popularity, development trends, and historical perspectives, offering a comprehensive understanding of the ZKP tooling ecosystem.
Digital systems face not a security failure but an ontological one. Authority on the internet is implemented as code, and code is inherently simulable, reproducible, and scalable. As artificial intelligence exposes this flaw at scale, efforts to secure digital authority through identity, credentials, and probabilistic verification prove structurally insufficient. This paper argues that authority cannot ontologically originate from code, and that all code-based authority systems are therefore structurally vulnerable, regardless of implementation quality. We outline the historical origins of the error, explain why vulnerability is unavoidable in code-based authority systems, and propose a return to presence as the only non-simulable foundation for digital authority—implemented through local cryptographic proof generation that preserves privacy by architectural design. The core claim is simple: code cannot be authority. Authority must arise from being. This is not a technological decision, but an ontological one—and ontological mistakes cannot be patched. Keywords: ontological cryptography, HISPU, digital trust, code-based authority, human presence verification, cryptographic attestation, privacy-preserving architecture, cybersecurity, authentication, biometric entropy, local processing, zero-knowledge presence, environmental embedding, physical unclonability, quantum-resistant, AI safety, digital sovereignty, proof of being, presence-based authority
In traditional financial processes, repetitive operations rely on manual intervention, which leads to efficiency bottlenecks and data tampering risks. This study generates standard operation sequences through RPA process mining and builds atomic operation units based on smart contracts. This paper transforms the distributed RPA controller to implement parallel contract calls and combines zero-knowledge proof to ensure cross-organizational data security. The blockchain status channel can be used to monitor anomalies in real time and trigger on-chain evidence storage, and the “execution-evidence-audit” closed loop can be formed through oracle docking supervision. The experiment shows that the processing cycle is shortened from an average of 20.76 hours for manual work to 6.23 hours, and the audit trail completeness rate is improved. Research has confirmed that the deep integration of RPA and blockchain can build an efficient, secure and reliable financial automation system, providing key technical support for digital transformation.
As the world transitions toward digital-first governance and civic participation, ensuring the security and integrity of voting systems has become a critical concern. Traditional evoting mechanisms, although convenient, suffer from a range of vulnerabilities - including voter impersonation, double voting, identity leaks, and tampering by insiders or external adversaries. ECC-EXONUM-eVOTING was previously proposed to mitigate many of these issues through elliptic curve cryptography (ECC), Zero-Knowledge Proofs (ZKP), and Exonum private blockchain. In this paper, we extend the capabilities of ECC-EXONUMeVOTING by integrating two novel modules aimed at enhancing both system intelligence and accessibility. First, we implement an AI-based fraud detection system using unsupervised anomaly detection techniques that proactively identify and block fraudulent voting behaviors in real time. Second, we introduce a secure offline voting architecture designed for voters in remote or lowconnectivity regions, using QR-based tokenization and Merkle-root-based integrity proofs for delayed blockchain synchronization. Through simulations, algorithmic validation, and comparative analysis, we demonstrate how these enhancements significantly increase the robustness, scalability, and real-world applicability of blockchain-based voting systems.
Conventional authentication techniques, such as one-time passwords and passwords, are extremely susceptible to data breaches, credential theft, and phishing attacks. These vulnerabilities are increased when using shared or public devices. This paper proposes a password-less authentication architecture for various environments and organization based on Zero-Knowledge Proofs in order to overcome these issues. The proposed model ensures that no sensitive credentials are sent or retained by having a user demonstrate that they possess a secret without disclosing it to the server. In doing so, the attack surface linked to traditional login methods is greatly reduced. The framework is meant to be scalable, lightweight and easy to integrate with learning management systems, corporate sites, online test platforms, and university websites.
Traditional health data infrastructure fragments longitudinal health status into isolated clinical encounters, introduces significant self-reporting bias, and concentrates data ownership among centralized custodians.This paper proposes an institutional research lab architecture-Proof of Health-that treats verified health status as a cryptographically attestable primitive suitable for decentralized trials, data marketplaces, and risk-adjusted health contracts.The architecture integrates three core components: (1) multi-modal longitudinal data collection via remote patient monitoring (RPM), wearable sensors, and structured clinical assessments; (2) privacy-preserving verification using off-chain encrypted storage paired with on-chain attestations and zero-knowledge proofs; and (3) decentralized trial infrastructure supporting hybrid recruitment, telemedicine visits, and electronic patient-reported outcomes (ePROs).We define a standardized "Proof of Health" metric derived from biomarker trajectories, behavioral adherence logs, and imaging-derived phenotypes, versioned using FHIR interoperability standards and blockchain-based metadata provenance.The lab architecture incorporates HL7 FHIR compliance, GDPR/HIPAA-aligned consent automation via smart contracts, and risk-based remote monitoring (RBM) protocols aligned with ICH-GCP guidelines.Initial pilot studies (N = 20-50 participants per cohort) will validate the Proof of Health signal across three use cases: (1) insurance risk stratification, (2) employment wellness contracts, and (3) participation in decentralized science (DeSci) research data marketplaces.Participants retain cryptographic custody of raw data while institutions gain provably valid, tamper-evident health intelligence.We present the system architecture, methodology, preliminary endpoint definitions, and regulatory pathways for pilot and confirmatory trials.This framework aims to resolve the central tension in modern health research: enabling rigorous longitudinal science while strengthening individual data sovereignty and consent transparency.
Maiesha Fahomida, Nushraq Nawer Hossain, Farhan Ahmad Nafis, Raian Islam
Modern digital communication requires stronger mechanisms for both confidentiality and authentication to mitigate threats such as impersonation, replay, and eavesdropping. Although traditional cryptographic methods offer secrecy, they lack strong identity verification in adversarial environments. To ensure security in both transmission and authentication, we proposed a hybrid framework combining the most effective mechanisms for secure communication, enhanced with LSB Steganography to conceal sensitive information. Zero-knowledge proofs are used for secure authentication. Diffie-Hellman with AES-256-GCM ensures confidentiality and data integrity, while LSB Steganography provides secure concealment of transmitted communication. Our method has been evaluated using a variety of techniques, including steganographic quality assessment, encryption-decryption performance testing, and authentication time measurement, confirming its resilience against common security risks. The proposed methods achieve PSNR values up to$\mathbf{7 4. 5 8 ~ d B}$and SSIM of 0.9999. The encryption time ranges from 0.035 ms to 0.068 ms, while the decryption time remains consistently lower, ranging from 0.008 ms to 0.015 ms. The results demonstrate that the proposed framework is a viable option for secure data transfer, as it guarantees confidentiality, integrity, authentication, and covert communication.
Nayana More, Sandeep Vanjale, Gauri R. Rao, Madhavi Mane
This study introduces a blockchain-based framework designed to strengthen the privacy, security, and verifiability of Electronic Health Records (EHRs) within Internet of Things (IoT)-driven healthcare environments. The proposed hybrid model combines blockchain for tamper-proof data logging, the InterPlanetary File System (IPFS) for scalable and efficient off-chain storage, and advanced cryptographic mechanisms such as smart contracts and Zero-Knowledge Proofs (ZKPs) to enable secure access management. Within this architecture, patients retain ownership and control of their encrypted medical data, while healthcare providers obtain permissioned access verified through ZKP-enabled smart contracts. Comparative evaluation reveals notable performance gains—92% enhancement in data integrity, 87% improvement in privacy protection, and a 30–35% reduction in unauthorized access—relative to conventional centralized EHR systems. Additionally, the framework demonstrates over 40% higher auditability and trust among healthcare entities. Remaining research challenges include achieving cross-platform interoperability, ensuring regulatory compliance, and integrating advanced privacy-preserving technologies such as federated learning and homomorphic encryption. Future work aims to optimize consensus mechanisms and align the framework with HL7 FHIR standards to facilitate scalability and real-world deployment.
The traditional credit scoring industry, dominated by a few centralized bureaus, suffers from opacity, data insecurity, and a lack of user-controlled data sovereignty. This paper introduces Zk-Cred, a novel decentralized protocol designed to address these challenges by leveraging a unique combination of Fully Homomorphic Encryption (FHE), Zero-Knowledge Proofs (ZKPs), and W3C Verifiable Credentials (VCs). Zk-Cred empowers individuals to generate a verifiable, privacy-preserving credit score without revealing their underlying financial data to any third party. The protocol’s core mechanism involves users encrypting their financial data client-side using an FHE scheme. A decentralized network of nodes then executes a publicly auditable credit scoring model on this encrypted data, computing a score that is only ever decrypted by the user. The user can then generate a ZKP to prove the correctness of the computation and receive a tamper-proof VC representing their creditworthiness. This VC can be presented to financial service providers, such as DeFi lending platforms or traditional institutions, for instant verification. By synthesizing these cryptographic primitives, Zk-Cred offers a new paradigm for credit scoring that is transparent, secure, and user-centric, with significant potential to enhance fairness and access in the global fintech ecosystem.
Bharath M. B, Ashwni S S, Mamatha M, Sowjanya S · 6 authors
With increasing dependence on AI for medical imaging diagnostics, privacy concerns and strict regulations continue to restrict data sharing across healthcare institutions. To address this, we propose a novel framework that enables cross-institutional collaboration without compromising sensitive patient information. Our system integrates federated learning with advanced privacy-preserving techniques, including homomorphic encryption, secure aggregation, differential privacy, and zero-knowledge proofs. Hospitals retain their data locally and contribute encrypted, noise-added model updates, ensuring that raw data never leaves the premises. Secure aggregation and encryption prevent any entity, including the central server, from accessing individual contributions. Differential privacy introduces mathematically bounded noise to mitigate risks from inversion and membership attacks. Meanwhile, zero-knowledge proofs allow clients to verify the legitimacy of their training process and updates without revealing internal computations or data. This layered privacy defense effectively counters gradient inversion, model poisoning, and membership inference attacks, all while maintaining strong diagnostic performance. Evaluated on real-world medical imaging datasets, our method balances accuracy with compliance to privacy laws like HIPAA and GDPR. The proposed architecture offers a scalable and trustworthy approach to enable AI-driven diagnostics across hospitals, ensuring patient confidentiality is never compromised.
Jiahao Li, Qiang Liu, Ray C.C. CHEUNG, Zhaohui Guo
Zero-Knowledge Proof (ZKP) has been widely applied in fields such as blockchain and privacy-preserving computing. However, the proof generation process remains computationally complex and time-consuming, which limits its further applications. Various schemes have been proposed to optimize the underlying modular operations with dedicated hardware support, but existing schemes still face low-efficiency problems. To address the problems, we propose an efficient Barrett modular multiplier design, especially for ZKP. Evaluation on a Xilinx XCVU9P FPGA shows that, compared to two existing pipelined designs, the proposed design improves throughput per slice by up to 20.4% and 49.6%, respectively, and achieves an $8.6 \times$ improvement over an existing non-pipelined design.