Karthikeyan Marappan, C L Annapoorani, Nazura Javed, Udhaya Sankar T P · 6 authors
Enhancing digital security has become essential in the age of decentralized systems. Zero-Knowledge Proof (ZKP) is a cryptographic method that allows one party to demonstrate ownership of certain knowledge without disclosing the actual data. When used with blockchain, Zero-Knowledge Proofs significantly improve privacy, validation, and data secrecy. This study investigates the use of Zero-Knowledge Proof in blockchain environments to enhance trustless authentication, secure transactions, and identity safeguarding. The main aim is to provide safe conversations while preserving transparency and anonymity. The immutable characteristics of blockchain, together with the privacy-preserving protocols of zero-knowledge proofs, allow applications like secret smart contracts, decentralized identification systems, and safe voting methods. The collaboration between blockchain and zero-knowledge proofs mitigates data exposure concerns and diminishes dependence on conventional centralized verification. This strategy aids in the development of robust digital infrastructures that can withstand cyber-attacks and data breaches. The incorporation of Zero-Knowledge Proof in blockchain signifies a progressive improvement in the protection of sensitive digital transactions across several industries.
Background and aims: Intensive care units (ICUs) face growing challenges with cybersecurity, data interoperability, medication safety, and resource management in an increasingly digital healthcare environment. This review explores how blockchain technology can address these issues and improve critical care delivery. Data sources: Relevant literature was sourced from peer-reviewed journals, healthcare cybersecurity reports, and studies on blockchain applications in medical settings. Study selection: Included works focused on blockchain's role in enhancing data security, drug traceability, consent management, and integration with AI tools in ICU contexts. Data synthesis: Blockchain offers tamper-proof health records, decentralized data sharing, and automated smart contracts, potentially transforming ICU operations. Benefits include improved patient safety, resource efficiency, and decision support. However, challenges such as scalability, regulatory concerns, and implementation costs remain. Conclusion: Blockchain holds strong potential to enhance ICU workflows and patient outcomes. Realizing its impact will require collaborative efforts and further research to overcome current limitations. How to cite this article: Gondode P, Dass C, Kumar S, Malviya A, Ashwin M, Khanna P. Blockchain in Critical Care. Indian J Crit Care Med 2025;29(6):525-530.
Gang Xu, Shiyuan Xu, Xinyu Fan, Yibo Cao · 7 authors
The Industrial Internet of Things (IIoT) has been widely studied, which dramatically enhanced the manufacturing efficiency and service elasticity. However, how to ensure the data confidentiality and security in the event-driven publish/subscribe communication model becomes a cumbersome problem. To address this concern, ring signatures have been researched deeply. Nevertheless, existing solutions have large computational burdens and neglect to incorporate reporting and tracing features, which makes it impractical for IIoT. In this way, research focus on designing an efficient report and traceable ring signature is still far-reaching. In this article, we propose RAT ring, a novel report and traceable ring signature, which provides publisher authentication, anonymous communication, reporting, and tracing. To achieve this, we adopt the zero knowledge proof to verify the authenticity of publisher data, and the signature of knowledge to trace the signature. Then, we formalize and prove the security of our scheme. Eventually, through comprehensive performance evaluation, our scheme outperforms prior works by approximately up to 51 times in terms of total computational overhead. These results demonstrate that our design is practical and effective for data privacy-preserving in IIoT.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.
In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers because they offer reliable and convenient access to blockchain data: operating a full node themselves requires significant computational and storage resources, and public (permissionless) RPC nodes lack financial incentives to serve large numbers of clients with consistent performance. Permissioned access to an otherwise permissionless blockchain network raises concerns regarding the privacy, integrity, and availability of data access. To address this, we propose a Permissionless Accountable RPC Protocol (PARP). It enables clients and full nodes to interact pseudonymously while keeping both parties accountable. PARP leverages "light client" schemes for essential data integrity checks, combined with fraud proofs, to keep full nodes honest and accountable. It integrates payment channels to facilitate micro-payments, holding clients accountable for the resources they consume and providing an economic incentive for full nodes to serve. Our prototype implementation for Ethereum demonstrates the feasibility of PARP, and we quantify its overhead compared to the base RPC protocol.
Ensuring privacy and security in healthcare data sharing is critical due to the sensitive nature of patient information and the growing threat of cyber attacks. This paper explores the development of privacy-preserving data-sharing protocols for healthcare systems by integrating cryptographic techniques and blockchain technology. The study aims to establish a secure framework that facilitates seamless data exchange among healthcare stakeholders while maintaining data integrity, confidentiality, and access control. Key cryptographic mechanisms, including homomorphic encryption, zero-knowledge proofs, and attribute-based encryption, are employed to ensure that only authorized entities can access patient records without exposing sensitive details. Blockchain technology is leveraged to create a decentralized and tamper-resistant ledger, ensuring transparency and auditability in data-sharing transactions. Smart contracts are utilized to enforce predefined access policies automatically, enhancing security and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The findings indicate that the proposed framework significantly mitigates risks associated with unauthorized access, data breaches, and single points of failure. Comparative analysis with traditional centralized systems demonstrates improved efficiency, scalability, and security in healthcare data management. The integration of blockchain and cryptographic techniques ensures robust privacy-preserving mechanisms without compromising accessibility or interoperability. This research provides a novel approach to secure data sharing in healthcare, fostering trust among stakeholders while ensuring compliance with privacy regulations. Future work will focus on optimizing computational efficiency and addressing scalability challenges to facilitate widespread adoption in real-world healthcare ecosystems.
Web-based credit card payments require complete disclosure of all payment card details for transaction authorization. The card’s CVV (Card Verification Value) is the secret code that authorizes card not presented transactions. Currently, all payment card details must be shared among various intermediaries involved in processing the transaction. To mitigate the risks associated with fraudulent transactions, industries have adopted security standards such as the PCI DSS. Credit card data confidentiality rests on all involved stakeholders adhering to best security practices, including data communication encryption, and do not misuse the payment information. However, this security posture does not prevent potential credit card data leaks. We propose an alternative method for conducting remote card payments that does not require disclosing the authorization code while ensuring high interoperability with existing payment networks. Our approach demonstrates how designated verifier Zero-Knowledge Proofs (ZKP) enable minimal disclosure of card details, particularly protecting the confidentiality of authorization codes.
Daria Schumm, Cedric von Rauscher, Katharina Olga Emilia Müller, Burkhard Stiller
Transparency and immutability of blockchains can expose metadata and raise concerns about its classification as personal data under privacy regulations. This paper investigates privacy risks associated with metadata in blockchain-based identity systems. Additionally, two privacy-preserving mechanism designs, namely Zero-Knowledge Proof (ZKP) and Homomorphic Encryption (HE), to protect metadata are proposed. As a result, this work introduces the first use case of HE privacy-preserving mechanism in the context of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems.
The Artificial Intelligence of Things (AIoT) is driving human society from “information” to “intelligence”, and the information technology industry is undergoing tremendous changes. However, AIoT data faces security threats such as leakage and illegal access when assisted by third parties. Therefore, some scholars use attribute-based proxy re-encryption (ABPRE) for secure sharing of data. However, the existing ABPRE schemes suffer from high computational overhead and inefficient attribution revocation, which seriously hinders practical application. To solve these problems, in this paper, we propose an efficient attribute-based proxy re-encryption scheme with fast attribute revocation (EPREAR). We design a non-interactive zero-knowledge proof protocol based on blockchain to ensure the verifiability of the key during attribute revocation. Furthermore, we devise a boundless encryption and decryption mechanism to enable the system's encryption and decryption with a fixed computation overhead, regardless of the size of the attribute set. And EPREAR possesses the ability to add infinite attributes without re-initializing the system. Finally, we perform theoretical and experimental analyses that show EPREAR has excellent computational performance. As a consequence, it has better application value in AIoT.
Management of secret keys for digital signatures is one of the most critical issues in decentralized applications. Since there is no administrator, losing a secret key can result in losing all assets or rights. To address this problem, fuzzy extractors and fuzzy signatures, which generate private keys directly from biometric information, have been considered in addition to conventional biometric authentication. However, these methods using biometric secret keys do not support group signatures. Therefore, it is not applicable to use cases that require consensus building by a specific community (group), such as DAO and DeFi.In this paper, we propose a new scheme for converting existing fuzzy signatures to group signatures using zero-knowledge proofs to address this problem. More precisely, we first define an anonymizable signature that is a generalization of a group signature and then convert a fuzzy signature into an anonymizable signature using an ordinary (classical) zero-knowledge proof. In addition, the signature data size is optimized to a constant size using zk-SNARK. Our implementation experiments show that our schemes achieve practical signature generation and verification times and signature sizes even for a group of up to 100,000 people. This paper’s results can be used to prevent the loss of secret keys and enable flexible DApps use cases.
Nai‐Wei Lo, Chi-Ying Chuang, Jheng-Jia Huang, Yuxuan Luo
With the rise of the Internet of Vehicles (IoV), secure and efficient authentication is essential to prevent cyber threats. This paper proposes a session key establishment protocol using Zero-Knowledge Proofs (zk-SNARKs) and Elliptic Curve Cryptography (ECC), including the Elliptic Curve Diffie–Hellman (ECDH) key exchange, to ensure privacy and efficiency. While zk-SNARK computations introduce additional verification overhead, our optimizations, such as precomputed proof parameters and lightweight session re-authentication, mitigate delays. Performance evaluation shows a 20% reduction in computation overhead and a 75% faster re-authentication time compared to existing methods, making it a secure and practical solution for real-world IoV applications.
Juan Alamrio Berrios Moya, John Ayoade, Md. Ashraf Uddin
Academic credential fraud presents a significant challenge to the global academic and labor markets, undermining the credibility of legitimate qualifications. In this paper, we introduce ZKBAR-V, a Zero-Knowledge Proof-Enabled Blockchain-Based Academic Record Verification System. This system is designed to provide a privacy-preserving, immutable, and secure framework for managing academic credentials. The proposed system leverages zkEVM smart contracts on a blockchain-based infrastructure that enables credential verification without exposing underlying data. The approach integrates Decentralized Identifiers (DIDs) to standardize identity management while eliminating reliance on centralized authorities. We have used dual-blockchain, which separates public and private information, which can enhance both efficiency and privacy. In addition, this approach employs the Interplanetary File System (IPFS) for decentralized and secure document storage. ZKBAR-V is designed as an open-source, interoperable solution with a standardized Application Programming Interface (API) for seamless integration. We implemented the system and conducted comprehensive testing, which demonstrates its capability to manage transactions securely, maintain privacy, and reduce costs compared to traditional Ethereum mainnet-based solutions. By combining advanced blockchain technologies, decentralized storage, and globally unique identifiers, ZKBAR-V offers a scalable, adaptable, and robust solution for academic credential management. This strategy can significantly enhance credential integrity, promote global student mobility, and provide institutions worldwide with a trustworthy and efficient verification system.
The major challenge in the existing communication system is maintaining the user's privacy while ensuring the pro- cess of verification and authentication. The conventional methods either jeopardize with user's privacy by linking the data or message to the source or fail to prevent false submissions because of weak authentication mechanisms. To address these issues, this paper proposes a Zero-Knowledge Proofs-based Quantum- resistant Anonymous and Authenticated Feedback System that optimizes Zero-Knowledge Succinct Non-Interactive Argument of Knowledge, shortly termed as zk-SNARKs, to enable secure, anonymous, and verifiable feedback submissions. The method presented in this research achieves strong authentication without sacrificing user privacy, which was not possible with traditional techniques like digital signatures, public-key infrastructure, and others. The system is resistant to impersonation and Sybil attacks because it uses zk-SNARKs to enable users to authenticate their permission to send feedback without disclosing their identity. Furthermore, the suggested framework is made to be postquantum secure, guaranteeing long-term resilience against sophisticated quantum attackers, since quantum computing poses a danger to traditional cryptographic techniques like RSA and ECC. Security, effectiveness, and practical viability of the system are assessed, based on which it is concluded that zk-SNARKs are a reliable and scalable basis for privacy-preserving feedback mechanisms and in various other applications like online dis- cussion forums, educational assessments. The study highlights how well zk-SNARKs succeeds in making a privacypreserving authentication system and mitigating the risk of quantum attacks.
Blockchain technologies are inherently decentralized and designed in such a way to ensure data integrity through cryptographic tools such as hashing, Merkle trees, and event consensus mechanisms involving PoW and PoS. This paper therefore develops an integrated model incorporating the aforementioned elements and hence provides a solid framework for real-time, effective, and scalable validation and security of data. The model ensures high compatibility with prevailing data systems to support efficient and scalable processes of validation. The framework, while in testing, has always returned consistent hash times and efficient recording to the blockchain, regardless of size. This flexible use of PoW and PoS makes it a tunable framework according to the needs of a particular application without sacrificing security or efficiency. This includes an anomaly detection system featuring a 93.5 % accuracy rate in robustly identifying abnormalities within the data authentication process.
The rapid proliferation of Internet of Things (IoT) devices has introduced significant challenges in maintaining data security, integrity, and privacy, particularly in dynamic and multi-user environments where sensitive information is frequently exchanged across distributed networks. Conventional security mechanisms often fall short in addressing the unique demands of IoT systems due to their limited scalability, vulnerability to advanced cyber threats, and the need for real-time data processing. To address these concerns, this paper presents a novel and secure data-sharing architecture that seamlessly integrates Homomorphic Proxy Re-Encryption (HPRE) with blockchain technology. The proposed HPRE mechanism empowers a trusted proxy to re-encrypt ciphertext for designated recipients without accessing the original plaintext, thereby enabling flexible, privacy-preserving information exchange among authorized users. This allows IoT data owners to maintain control over their data while securely delegating access rights in a controlled and verifiable manner. To enhance accountability and eliminate single points of failure, a blockchain-based ledger is employed to immutably record all encryption operations, key-sharing events, and data access transactions. This integration guarantees transparency, traceability, and tamper-resistance, fostering a trustless environment suitable for highly sensitive IoT applications such as healthcare monitoring, industrial automation, and smart city infrastructures. Additionally, by incorporating lattice-based cryptography within the HPRE framework, the system achieves strong post-quantum security guarantees, rendering it resilient against both classical and emerging quantum computing threats. The proposed architecture is designed to be scalable, lightweight, and adaptable to heterogeneous IoT ecosystems, making it a reliable and future-proof solution for secure data exchange in privacy-critical applications. Experimental results and security analyses demonstrate the effectiveness and efficiency of the system in real-world IoT scenarios, confirming its potential for widespread adoption in next-generation secure IoT networks.
Abstract: Federated deep learning (FDL) is an emerging paradigm that enables multiple decentralized devices or institutions to collaboratively train a shared model while keeping data localized. This approach preserves privacy, reduces communication overhead, and complies with data governance regulations. In this paper, we explore the implementation and performance of FDL in real-world scenarios such as healthcare, finance, and IoT systems. Utilizing frameworks like TensorFlow Federated, PyTorch, and interpretability tools like SHAP and LIME, we evaluate FDL against centralized deep learning models. We analyze convergence rates, model accuracy, data privacy risk, and computational efficiency. Regression and predictive analyses reveal that FDL can retain over 90% accuracy of centralized models with significantly enhanced data security. Keywords: Federated Learning, Deep Learning, Privacy Preservation, Decentralized Training, TensorFlow Federated, Secure AI, SHAP, LIME, Model Interpretability
Decentralized applications are often composed of multiple interconnected smart contracts. This is especially evident in DeFi, where protocols are heavily intertwined and rely on a variety of basic building blocks such as tokens, decentralized exchanges and lending protocols. A crucial security challenge in this setting arises when adversaries target individual components to cause systemic economic losses. Existing security notions focus on determining the existence of these attacks, but fail to quantify the effect of manipulating individual components on the overall economic security of the system. In this paper, we introduce a quantitative security notion that measures how an attack on a single component can amplify economic losses of the overall system. We study the fundamental properties of this notion and apply it to assess the security of key compositions. In particular, we analyse under-collateralized loan attacks in systems made of lending protocols and decentralized exchanges.
Eber J. Ávila-Martínez, G L K Niharika, Arutchelvi Jayaraj
Voting is the most important topic for societal concern. Nowadays voting is considered as important and mandatory for all the citizens of India. So, compared with the traditional methods, E Voting is considered as the best option for voting mechanism as it is cost efficient, easy accessibility and convenient for every citizen in India. Therefore, E-Voting is mainly focused on Security and these security issues are considered as major drawbacks. To eradicate this factor, Blockchain came up with the idea of Quantum-Resistant Zero-Knowledge Proof Algorithm (QRZ-KPA). QRZ-KPA uses cryptographic and hashing strategies to make a secure and safe voting process. It also safeguards unauthorized users and voters to access it by ensuring the authenticity of the voter without revealing the personal information of the voters by using zero knowledge algorithm. Thus, the QR-ZKPA algorithm ensures the safe and integrated voting process and becomes a strong solution for the challenges and drawbacks faced by the online voting system. Hence, this algorithm provides the safe voting process in democratic process. Furthermore, QR-ZKPA is stronger with the combination of the Isolation Forest (iForest) machine mastering algorithm to identify irregularities and anomalies in vote casting styles, thereby similarly fortifying the system’s defenses in opposition to false sports. This technique offers good protection against vote manipulation, double balloting, unauthorized entry to, and other capability dangers. Our assessments illustrate the combined effectiveness of QR-ZKPA and iForest in enhancing the integrity, confidentiality, and security of blockchain-based totally e-balloting systems, guaranteeing their durability and dependability in a put upquantum environment.
To tackle the privacy protection and efficiency challenges within the block-chain domain, this chapter introduces a privacy-enhancing solution for copyright blockchains, integrating lightweight homomorphic encryption and zero-knowledge proofs. This innovative approach enhances homomorphic encryption algorithms to streamline key generation and encryption processes while incorporating zero-trust security principles to curtail unnecessary homomorphic operations. Following the application of lightweight homomorphic encryption, sensitive data is transformed into ciphertext and securely added to the blockchain ledger by nodes authorized for accounting purposes. This solution not only rectifies the inherent drawbacks of complete data transparency in blockchain networks but also enhances operational efficiency. Security analysis underscores its qualities, including resistance to tampering and data privacy preservation. Through both performance simulations and theoretical deductions, the paper demonstrates that this approach mitigates efficiency challenges related to the distribution, sharing, and computation of private data in ciphertext form. Ultimately, this proposed methodology proves more effective in upholding customer privacy than traditional digital copyright models.
Electronic voting (e-voting) has emerged as a transformative technology in the modern digital era. Many countries across the world are using e-voting systems in different types of elections, from political to non-political. One of the primary goals of e-voting is ensuring both verifiability and privacy simultaneously, which we refer to as security. Verifiability is a security feature that guarantees voters can confirm their vote is reflected in the final election result, while privacy guarantees that no one is able to link a vote to the voter who cast it. Verifiability needs to hold only for the duration of the election, whereas privacy needs to extend beyond the election period, even decades after the election. This property, known as everlasting privacy in the literature, ensures that even computationally unbounded adversaries cannot compromise voter privacy, securing elections against future advances in computing, including quantum computing. Researchers have proposed a wide variety of protocols to achieve this ambitious goal in secure e-voting, however, these protocols differ significantly, making the analysis and state-of-the-art complicated. In this thesis, we first address this fragmentation by systematically analyzing all existing e-voting protocols designed to ensure everlasting privacy. We map out the relationships and dependencies among these protocols, evaluate their security and efficiency under realistic assumptions, and identify unresolved challenges in the field. Our work provides a foundational reference for researchers aiming to design secure e-voting systems with everlasting privacy, paving the way for privacypreserving elections in the post-quantum era. Building on these insights, we propose a novel e-voting system that integrates the best practices from prior research while addressing their limitations. Leveraging the Hyperion scheme as a foundation, we develop an enhanced protocol that not only guarantees everlasting privacy but also introduces everlasting receipt-freeness and coercion mitigation. Unlike existing systems like Selene and Hyperion, which rely on computational assumptions for privacy, our protocol offers privacy even against adversaries with unlimited computational power. In secure electronic voting systems with everlasting privacy, the focus is on futureproofing privacy, while sometimes election verifiability relies on the computational soundness of zero-knowledge proofs (ZKP), which are vulnerable to quantum adversaries. Therefore, a key technical challenge is designing e-voting systems with efficient post-quantum cryptographic primitives to secure both privacy and verifiability against quantum attacks. In this thesis, we advance the state of post-quantum ZKPs by focusing on the ZKPs proposed by Jain et al., which are based on the conservative Learning Parity with Noise (LPN) assumption. We optimize the efficiency of these ZKPs, achieve formal security verification using EasyCrypt, and uncover flaws in existing implementations, demonstrating their vulnerability to malicious provers. Additionally, we construct the first code-based ZKP of shuffle, enabling a verifiable and privacy-preserving e-voting protocol with mixing-based tallying. Our e-voting system ensures both verifiability and vote privacy through the computational difficulty of decoding random linear codes, marking it as the first verifiable code-based e-voting system.
Federated Learning (FL) enables collaborative model training across hospitals while keeping patient data local, thus aiming to satisfy strict healthcare privacy regulations (e.g. HIPAA, GDPR). However, FL still leaks information via shared model updates, exposing it to membership inference and gradient inversion attacks. In this work, we propose an end-to-end framework that integrates zero-knowledge proofs (ZKPs) with FL to ensure both data privacy and trust in the aggregation process. In our design, each hospital (client) sends encrypted model updates to a central aggregator, which then computes the global model and simultaneously generates a succinct ZKP (e.g. a zk-SNARK) attesting to the correctness of the aggregation. Clients (or a verifier network) can efficiently verify this proof without learning any additional information. We simulate a disease-prediction task on synthetic medical data and evaluate metrics including predictive accuracy, proof generation/verification time, and communication overhead. Our results (see Table 1 and Fig. 3) show that incorporating ZKP maintains almost identical model accuracy compared to standard FL while adding moderate computational and bandwidth overhead. ZKP verification costs scale favorably (often <50% of proof generation time) and can be offloaded to a blockchain network to avoid burdening resource-constrained hospitals. The key contribution is a structured ZK-FL framework combining FL and zk-SNARKs, along with a formal threat model. This approach closes FL’s trust gap in healthcare settings, and suggests future work on scalable proof systems (e.g. post-quantum ZKPs) and integration with blockchain-based verifiers.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain's performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework's capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications.