This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential.
In IoT(Internet of Things) Traditional authentication methods, such as passwords or public-key cryptography, often encounter challenges related to security, efficiency, and scalability. In response, Present work proposed a streamlined alternative utilizing Zero-knowledge proofs(ZKPs), allowing users to prove their identity with minimal exposure to sensitive information. Emphasizing the need for optimization in computational resources, this approach becomes particularly valuable in the context of Internet of Things application(IOTA). Current work introduces a novel method for authentication that combines the concept of isomorphic graphs from multi-graph ZKP with ZeroKnowledge Succinct Non-Interactive Argument of Knowledge (Zk-SNARKs), along with multi-threading in the IOTA ecosystem. This advancement represents a significant step forward in scalability and provides a solution to the increasing need for secure and effective authentication methods.
Hao Zhou, Changxu Liu, Lan Yang, Li Shang · 5 authors
Recently, there has been increased emphasis on privacy-preserving computation technologies, such as homomorphic encryption (HE) and zero-knowledge proof (ZKP). Modular multiplication is a critical component for both HE and ZKP. Variable bit-width is a must for many applications of privacy-preserving computation, due to variable bit-width requirements for different cryptography schemes. However, the majority of modular multipliers that support variable bit-width configurations exhibit relatively low throughput. This work presents a fully pipelined Montgomery modular multiplier with variable bit-width support. Truncated multipliers are introduced to reduce the resources of modular multipliers in our approach. In order to meet different bit-width requirements, the proposed modular multiplier can be dynamically reconfigured. The proposed design can support widely used bit-width configurations, specifically, 384-bit, 256-bit, and 128-bit. 256-bit and 128-bit modes support parallel computation of 2 and 6 sets of operands, respectively. Compared with existing variable bit-width modular multipliers, the proposed reconfigurable modular multiplier significantly improves the throughputs with even lower resources.
Kulvinder Singh, R. P. S. Tomar, Vansh Chaudhary, Mridul Jain
The cloud-based meteorological data management offers vast potential for research and collaboration, but inherent privacy concerns and data integrity risks necessitate robust solutions. This research suggests a new, multi-layered method to improve integrity and privacy in this field. By utilizing homomorphic encryption, this study makes it possible to perform calculations on encrypted meteorological data while keeping private information hidden from cloud service providers. Federated learning promotes cooperation without compromising the privacy of raw data by enabling distributed learning on local datasets. Lastly, zero-knowledge proofs are integrated to confirm the accuracy of computations made on encrypted data, ensuring the validity of outcomes without disclosing the underlying data. This integrated strategy presents a viable path for reliable and safe cloud-based meteorological data administration, promoting cutting-edge study and creativity while protecting private data.
In order to achieve zero-knowledge proof (ZKP) in distributed mobile scenarios, we propose a two-stage multi-prover ZKP framework. Our method utilizes secure multi-party computation (MPC), which has advantages such as flexible adaptation, stable performance, and fewer restrictions compared to existing solutions. In addition, based on the properties of cyclic groups, we optimize secure multi-party summation, improving the balance between security and efficiency, as well as transferability of the algorithm.
Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated learning is vulnerable to poisoning attacks, which can not only decrease the model performance, but also implant malicious backdoors. In addition, direct submission of local model parameters can also lead to the privacy leakage of the training dataset. In this paper, we aim to build a privacy-preserving and Byzantine-robust federated learning scheme to provide an environment with no vandalism (NoV) against attacks from malicious participants. Specifically, we construct a model filter for poisoned local models, protecting the global model from data and model poisoning attacks. This model filter combines zero-knowledge proofs to provide further privacy protection. Then, we adopt secret sharing to provide verifiable secure aggregation, removing malicious clients that disrupting the aggregation process. Our formal analysis proves that NoV can protect data privacy and weed out Byzantine attackers. Our experiments illustrate that NoV can effectively address data and model poisoning attacks, including PGD, and outperforms other related schemes.
Abstract Atomic Cross-Chain Swap (ACCS) is one important topic in cryptocurrency, where users can securely and trustlessly exchange assets between two different blockchains. However, most known ACCS schemes assume specific scripting functionalities of the underlying blockchains, such as Hash Time Locked Contracts (HTLC). In addition, these schemes are typically only applicable to certain digital signature schemes, like Schnorr or Elliptic Curve Digital Signature Algorithm (ECDSA) signatures. In this paper, we propose a generic ACCS scheme, independent from the underlying blockchains. To the best of our knowledge, this is the first solution of this kind. Our results are as follows. First, we define a formal system model of ACCS. Next, we present a generic ACCS scheme meets our model. This scheme admits atomicity in cross-chain swaps without the need for a Trusted Third Party (TTP) and protects users’ privacy. Finally, by using the Non-Interactive Zero-Knowledge (NIZK) proof protocol as a tool, we instantiate our generic scheme for Elliptic Curve Discrete Logarithm Problem-based (ECDLP-based) signatures. In addition, we implement our scheme, and the experimental results show that our protocol outperforms the existing ACCS schemes, such as the HTLC-based schemes.
The core premise of permissionless blockchains is their reliable and secure operation without the need to trust any individual agent. At the heart of blockchain consensus mechanisms is an explicit cost (e.g., mining cost) for participation in the network and the opportunity to add blocks to the blockchain. A key rationale for that cost is to make attacks on the network, which could be theoretically carried out if a majority of nodes were controlled by a single entity, too expensive to be worthwhile. We demonstrate that a majority attacker can successfully attack with a negative net cost when accounting for the mining rewards the attacker collects during the attack. This shows that the protocol mechanisms are insufficient to create a secure network, emphasizing the importance of socially driven mechanisms external to the protocol. At the same time, negative cost enables a new type of majority attack that is more likely to elude external scrutiny. This paper was accepted by David Simchi-Levi, finance.
Vladlen D. Afonin, Sergey Zapechnikov, Igor A. Prostov
Zero-knowledge proofs are being increasingly applied to a wide range of tasks in modern distributed information systems. Of particular interest are such areas of activity as digital asset management systems, anonymous electronic voting systems, and anonymous credentials. Nevertheless, within the framework of the desire of Russian developers to localize the developed products, there is a question of compliance of the used algorithms with the legislative framework of the Russian Federation, which obliges developers to use local cryptographic standards. As our analysis shows, insufficient attention has been paid in the literature to the applicability of these standards to the scenario of use in zero-knowledge proof systems. In particular, the complexity of proof generation, parameters of arithmetic schemes are not analyzed, there is no comparison of computational complexity and cryptographic properties with foreign alternatives. In this paper we consider in detail the peculiarities of implementation of the arithmetic scheme for the function of GOST 34.11-2018 in the most widespread language for arithmetic circuit programming Circom. The developed program code is open and available for use and modification. The characteristics of the scheme, compilation and generation times are analyzed. The obtained results were compared with other popular hash functions: the cryptographic hash function SHA256 included in the standard language library, and the hash function Poseidon, specialized and optimized for use in zero-knowledge proof systems. The results show that while the use of the Russian hash function is possible, it is not desirable in applications that do not require the use of local cryptographic standards, due to the greater time complexity of witness generation and consequently proof generation. Recommendations on usage scenarios are given and further research directions are suggested.
Venkata Naga Rani Bandaru, P. Visalakshi, L. N. Prakash Kumar Ponnuru, Shaik Mohammad Rafee · 5 authors
The synergy between machine learning and encryption fortifies data security and privacy. This comprehensive overview delves into pivotal encryption methods in ML, spotlighting their inherent adaptability and paramount role in shielding sensitive data. Differential privacy injects controlled noise, ensuring privacy preservation while upholding data utility, especially vital in healthcare and financial sectors. Federated learning facilitates decentralized training, while homomorphic encryption assures secure data processing. Secure multi-party computation (SMPC) empowers collaborative private computation, and zero-knowledge proofs authenticate veracity sans data exposure, pivotal in blockchain and identity validation. These sophisticated algorithms cater comprehensively to diverse security requisites, bolstering data protection across indispensable domains.
The relationship between doctors and patients is reinforced through the expanded communication channels provided by remote healthcare services, resulting in heightened patient satisfaction and loyalty. Nonetheless, the growth of these services is hampered by security and privacy challenges they confront. Additionally, patient electronic health records (EHR) information is dispersed across multiple hospitals in different formats, undermining data sovereignty. It allows any service to assert authority over their EHR, effectively controlling its usage. This paper proposes a blockchain enforced attribute-based access control in healthcare service. To enhance the privacy and data-sovereignty, the proposed system employs attribute-based access control, zero-knowledge proof (ZKP) and blockchain. The role of data within our system is pivotal in defining attributes. These attributes, in turn, form the fundamental basis for access control criteria. Blockchain is used to keep hospital information in public chain but EHR related data in private chain. Furthermore, EHR provides access control by using the attributed based cryptosystem before they are stored in the blockchain. Analysis shows that the proposed system provides data sovereignty with privacy provision based on the attributed based access control.
The public blockchain lacks data confidentiality. Although a level of anonymity seems guaranteed, it is still possible to link transactions and disclose related information. A solution to the privacy problem is to use cryptography in transactions, however this can lead to increased costs and slowdown in network throughput. Recent works experiment with advanced cryptography, in particular Zero-Knowledge proofs (ZK-proofs) can be supplied within a transaction to prove its validity, without revealing sensitive information. We analyze solutions that adopt ZK-proofs, such as Confidential Transactions (CTs). Several challenges emerge depending on both the zero-knowledge system and the balance model considered (UTXO, hybrid or account model). For ZK-proofs, systems that do not introduce additional trust are required. On the other hand, the account model is the most flexible for addressing security challenges. Moreover, CTs do not fully exploit the potential of ZK-proofs, since each transaction comes with one or more ZK-proof for a single transfer. Within this paper, we present ZeroMT, a novel multi-transfer private payment scheme for account-based blockchains. Drawing inspiration from Zether, our approach extends their work to develop a payment model that supports multiple payees within a single transaction. This also benefits scalability: ZeroMT enriches the CTs with the aggregation property, i.e., the batch verification of multiple transfers from a single and aggregate proof. We show that in our extended model the overdraft-safety and privacy security properties still hold. We provide an implementation and evaluation of ZeroMT, which shows the benefits of aggregating multiple transfers.
Smart healthcare systems play a pivotal role in delivering accessible medical services, especially in remote environments where patients rely on wearable devices to collect medical data, subsequently transmitted to caregivers for diagnosis purpose. Despite the benefits of telehealth systems, their vulnerability to security breaches stemming from insecure communication channels highlights the critical need for robust mechanisms ensuring secure data transmission and access control. These mechanisms are essential to verify the legitimacy of both the patients and caregivers. To address these challenges, we propose ZKP-MAC, a lightweight Zero Knowledge proof-based authentication and access control scheme specifically tailored for Telehealth systems. Our proposed scheme integrates physical unclonable functions (PUFs), leveraging cryptographic keys derived from device hardware fingerprints to authenticate patient data. Additionally, we adopt a zero-knowledge proof protocol for caregivers' authentication and access control. By regenerating keys based on device hardware fingerprints, our approach ensures lightweight, secure, and authentic communication. Additionally, our model enables dynamic key management mechanism to regulate data access effectively and continuously in telehealth applications. Experimental results demonstrate the robustness of our approach against various security threats, offering features such as perfect backward secrecy and mutual access control. We conduct a comprehensive evaluation of the ZKP-MAC's security attributes, and computational overhead, showcasing reduced complexity compared to competing schemes while maintaining resilience against attempts to compromise security features by both individual and colluding actors. Furthermore, our scheme undergoes rigorous formal analysis using Automated Verification of Internet Security Protocols and Applications (A VISP A).
With rapidly increasing drug abuse across the world, it is imperative to monitor their supply chain with sufficient transparency. Blockchain is a common solution for achieving transparency in supply chain monitoring, but it does not have sufficient throughput for large-scale supply chains. It is challenging to achieve throughput and privacy simultaneously because complex dependencies among the supply chain events and the need for aggregation both make the application of ZKP challenging. We present PrivHChain, a privacy-preserving hierarchical blockchain that preserves transaction privacy even against blockchain peers while allowing them to verify record consistencies. This is enabled by novel modeling of supply chain events which makes it possible to use novel efficient zero-knowledge protocol schemes to verify the complex dependencies. Novel aggregation techniques are proposed to enable the proof aggregation, and the proofs are used to design monitoring protocols. PrivHChain is implemented and validated with extensive experiments and simulations. The results indicate that (i) the extra overhead of encryption and ZKP schemes is acceptable or negligible, and (ii) the throughput is improved by up to 5 times in simulations even with all the encryption/ZKP schemes.
Rahma Mukta, Hye-Young Paik, Qinghua Lu, Salil S. Kanhere
We propose CredAct, a user activity verification designed with data minimisation to protect privacy. Many Benefits Schemes, such as discount offers, loyalty programs, and incentive systems, require verification of user activity (e.g., buying healthy food, step counts) in their business processes. These service providers can collect a large amount of users’ personal information, and often users do not have fine-grained control over the scope of data disclosure. In CredAct, we propose a Self-Sovereign Identity based framework implemented on blockchain that enables users participating in a benefits scheme to minimise data sharing during the submission and verification of data. We use a smart contract-based function along with a Zero-Knowledge Proof cryptographic commitment scheme, that forces the entities involved in the business process to collect or disclose only the required (minimum) data to fulfill the intended purpose. The evaluation shows that the system is feasible with minimal operational overheads compared to traditional cryptographic techniques. We also perform a qualitative privacy and security analysis considering relevant threats to CredAct.
This paper introduces ZK-Team, a solution addressing the challenges faced by organizations in enabling shared ownership of accounts among team members. The conventional method involves smart contracts, but usability issues and privacy concerns hinder practicality. ZK-Team leverages Ethereum’s Account ion (ERC-4337) and zero-knowledge proofs to allow shared accounts without dedicated EOAs (Externally Owned Accounts) for gas fees, ensuring privacy. Our solution includes an innovative mechanism for transparently managing zero-knowledge inputs, minimizing on-chain storage without relying on an off-chain storage. The paper discusses both the theoretical aspects of the solution and its implementation available as an open-source project.
In blockchain based decentralized finance, cross-chain payment channel is a crucial component for achieving the interoperability among different software-defined blockchain systems. However, there are still some challenges in existing payment channels scenarios. First, payment channels need to be established through intermediaries with service fees, but no one has developed a suitable strategy for pricing the service fees currently. Second, these scenarios disclose sensitive information when intermediaries are engaged with cross-chain requests. Last but not least, the payment channel established currently tend to lack robust mechanisms for protecting the details of transactions during trading phase. To address the above challenges, this paper proposes a zero knowledge empowered payment channel to enhance privacy and transaction details protection with intermediary pricing strategy. Specifically, we develop the pricing strategy based on GARCH model with market dynamics. Moreover, we design a mechanism based on Pedersen commitments for verifiable proofs to preserve privacy in cross-chain requests. Finally, we construct the payment channel with zk-SNARKs to guard transaction details during trading phase. The experiment demonstrates our approach develops optimal pricing strategy and offer enhanced privacy and anonymity assurances.
Jan Lauinger, Serhat Bezmez, Jens Ernstberger, Sebastian Steinhorst
Latest identity systems rely on public blockchains to enhance user autonomy and reduce tracking from conventional identity providers. At the same time, identity systems integrate novel technologies such as zero-knowledge proofs (ZKPs) to improve data privacy and data compliance. We show that a naive verification of ZKPs at smart contracts enables replay attacks: Attackers can replay ZKPs at arbitrary times without having access to the private inputs that are required for the computation of the ZKP. To solve this problem, we construct a transaction sequence which verifies time-bound and replay-resistant ZKPs at smart contracts. Our construction introduces an additional but constant fee of 0.14${\$}$ per verification of a ZKP on the public blockchain Ethereum. With our new construction, we propose Portal, a novel identity system for decentralized single sign-on.
We explain how to extend the Bitcoin backbone model of Garay et al. (Eurocrypt, 2015) to accommodate for redactable blockchains. Our extension captures fluid blockchain-based databases (with mutability requirements) and compliance with existing legislation, such as the GDPR right to be forgotten, or the need to erase offending data from nodes’ databases that would otherwise provoke legal shutdowns. Our redactable backbone protocol retains the essential properties of blockchains. Leveraging zero-knowledge proofs, old data can be erased without requiring trusted third parties or heuristics about past chain validation. Our solution can be implemented on Bitcoin immediately without hard-forks, and it is scalable. It allows the redaction of data from UTXOs or unconfirmed transactions that have not yet flooded the network, while guaranteeing invariance of the Bitcoin state. Thus, offending data does not need to persist in the system, not even temporarily.
With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.
This work focuses on utilising Physically Unclonable Functions (PUFs) for device authentication, exploiting a device's unique manufacturing-induced hardware variations. Traditional PUF-based authentication methods often rely on trusted third parties for validation or necessitate that Verifiers maintain large databases. Existing approaches that aim to reduce storage demands by reutilizing information typically address only networklevel threats, leading to doubts about the necessity of PUFs, or they focus exclusively on adversaries aiming at non-volatile memory. This paper introduces a classification guideline that delineates the scenarios in which PUFs are necessary or advantageous. Additionally, we present a novel PUF-based authentication scheme that incorporates challenge concealment to safeguard against comprehensive invasive physical attacks. This method offers perfect hiding, an enhanced level of security compared to previous models that permitted the reusing of PUF challenges. Through this approach, we aim to provide a more secure yet efficient framework for PUF-based authentication, addressing the limitations of current methodologies and extending the protection against a broader spectrum of adversaries.
Physical Unclonable Functions (PUFs) and Hardware Security