Blockchain Papers

Jens Ernstberger, Stefanos Chaliasos, George Kadianakis, Sebastian Steinhorst · 8 authors

No abstract is available for this record.

Alberto Ballesteros-Rodríguez, Salvador Sánchez‐Alonso, Miguel‐Ángel Sicilia

The widespread integration of on-demand services founded on proprietary algorithms into various software applications has ushered into a new era of advanced service capabilities. However, using these services entails disclosing information by the customer, not only during the payment process but also when using the service, where certain personal information must be shared to obtain a more personalized service. This practice potentially exposes users to increased security risks in case of data security breaches. In this paper, we introduce a novel framework aimed at enhancing client privacy and ensuring service integrity within the context of computing services that rely on proprietary algorithms. A blockchain-based approach is proposed to enhance user privacy throughout service provision, encompassing both the payment process and the verification of the provided service. Our proposal leverages properties of distributed ledger networks to improve user privacy during payment transactions and incorporates a verification system using zero-knowledge proofs on blockchain to validate the integrity of the contracted service. Finally, we analyze the privacy, overhead, and performance aspects of the framework, employing custom proprietary algorithms. We illustrate this through examples of Convolutional Neural Networks with multiple layers, undisclosed to the client. This emphasizes the potential benefits of its applicability for both service providers and clients.

Engin Zeydan, Luis Blanco, Josep Mangues‐Bafalluy, Şuayb S. Arslan · 7 authors

Blockchain network (BCN)-based Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federated Learning (FL), on the other hand, provides a collaborative framework to update Machine Learning (ML) models without relying explicitly on data exchange between the users. This paper investigates identity management and authentication for vehicle users in the context of FL. We propose a novel approach based on blockchain-based SSI, which focuses on maintaining the authenticity and integrity of vehicle users’ identities and data exchanged between the users and the aggregation server during the execution of the FL iterations. A primary objective of this paper is to achieve shorter durations for credential operations in an FL setting as the system size scales out. Integrating BCN-based SSI into the FL framework addresses several critical FL challenges, ensuring enhanced system security and operational integrity. This synergy of BCN-based SSI with federated learning enables robust identity verification providing a solution to fundamental trustworthiness issues in FL without sacrificing the benefits of decentralized data control, improving both the performance and reliability of the FL system. Experimental results suggest that the proposed FL-based system, together with credential management on a blockchain platform, has the potential to significantly improve data integrity and ensure the authentication of users. More specifically, the results of the FL system demonstrate that it takes longer (on the order of a hundred seconds) as the number of rounds and clients increase, while the implemented Decentralized Identifier (DID) system relying on BCN-based SSI has dramatically shorter dedicated time for completing credential operations.

Chao Liu, Cankun Hou, Tianyu Jiang, Jianting Ning · 6 authors

Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system (https://github.com/cliu717/AsynchronousStorage) offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

Francesca Stabile, Walter Lúcia, Amr Youssef, Giuseppe Franzè

The proliferation of cloud computing technologies has paved the way for deploying networked encrypted control systems, offering high performance, remote accessibility and privacy. However, in scenarios where the control algorithms run on third-party cloud service providers, the control’s logic might be changed by a malicious agent on the cloud. Consequently, it is imperative to verify the correctness of the control signals received from the cloud. Traditional verification methods, like zero-knowledge proof techniques, are computationally demanding in both proof generation and verification, may require several rounds of interactions between the prover and verifier and, consequently, are inapplicable in real-time control system applications. In this paper, we present a novel computationally inexpensive verifiable computing solution inspired by the probabilistic cut-and-choose approach. The proposed scheme allows the plant’s actuator to validate the computations accomplished by the encrypted cloud-based networked controller without compromising the control scheme’s performance. We showcase the effectiveness and real-time applicability of the proposed verifiable computation scheme using a remotely controlled Khepera-IV differential-drive robot.

Šeila Bećirović, Irfan Prazina, Damir Pozderac, Razija Turčinhodžić Mulahasanović · 5 authors

Digital credentials represent crucial elements of digital identity on the Internet. Credentials should have specific properties that allow them to achieve privacy-preserving capabilities. One of these properties is selective disclosure, which allows users to disclose only the claims or attributes they must. This paper presents a novel approach to selective disclosure BLS-MT-ZKP that combines existing cryptographic primitives: Boneh-Lynn-Shacham (BLS) signatures, Merkle hash trees (MT) and zero-knowledge proof (ZKP) method called Bulletproofs. Combining these methods, we achieve selective disclosure of claims while conforming to selective disclosure requirements. New requirements are defined based on the definition of selective disclosure and privacy spectrum. Besides selective disclosure, specific use cases for equating digital credentials with paper credentials are achieved. The proposed approach was compared to the existing solutions, and its security, threat, performance and limitation analysis was done. For validation, a proof-of-concept was implemented, and the execution time was measured to demonstrate the practicality and efficiency of the approach.

Ghassan Al-Sumaidaee

The digitization of healthcare presents both opportunities and challenges in managing medical data securely and efficiently. Centralized systems often raise concerns about data security, privacy, and accessibility. Blockchain technology, with its decentralized and transparent framework, offers a potential solution for data exchange and management. However, interoperability limitations between independent blockchain networks restrict their widespread adoption in healthcare. Fragmented data across these systems can lead to duplication, inconsistencies, and compromised patient privacy.This thesis proposes a novel approach using blockchain tokens, specifically non-fungible tokens (NFTs), which are unique digital certificates of ownership recorded on a blockchain, as a universal encrypted access system. These tokens facilitate the seamless transfer of patients' medical data across various blockchain-based healthcare networks. Assigning a unique token to each patient addresses the issues of data fragmentation and lack of interoperability that currently impede healthcare blockchain adoption.To achieve universal access with tokens, our solution leverages distributed storage systems (DSS), a decentralized network approach for data storage, employs steganography, a technique for hiding information within a digital carrier, and integrates cryptographic techniques. DSS eliminates centralized storage of sensitive medical data, thus preventing a single point of failure. To mitigate the inherent vulnerability of public DSS and NFTs metadata, steganography is employed to protect sensitive data, unique identifiers, and critical metadata, thus limiting access to authorized parties. Data is further secured with a password when embedded within images, serving later as NFTs images for simplified data retrieval. Cryptographic methods ensure secure password transfer during patient mobility across networks. Our model leverages the unique properties of NFTs. We use the mutability of NFT data to introduce patient updatable NFTs that can accommodate dynamic medical data. Conversely, the immutability of NFT data establishes an ownership mechanism for patients, ensuring data privacy, uniqueness, and a trusted, sharable protocol. This approach facilitates the seamless integration of new patient records in real-time on the public blockchain. A scoping study on a medical test network assessed the feasibility of using a private blockchain for healthcare records management (HRM). Insights gained led to expanding the scope to two blockchain medical networks. Multiple institutions implemented and tested medical record integration on IPFS within the private network using updatable NFTs on a public blockchain. Performance analysis confirmed the solution's feasibility, effectiveness, and potential for practical application

Tiphaine Henry, Sara Tucci-Piergiovanni

No abstract is available for this record.

Jules Maire, Damien Vergnaud

No abstract is available for this record.

Vincenzo Botta, Michele Ciampi, Emmanuela Orsini, Luisa Siniscalchi · 5 authors

No abstract is available for this record.

Zhanwen Chen

The proposal of the first cryptocurrency Bitcoin brings about the new technology called blockchain that massively expands the concept of financial system.Tamper-resistance, decentralization and traceability are three essential properties of a blockchain.In the following years, the original idea of blockchain also inspired the development and research of other cryptocurrency implementations.And the development of blockchain has extended beyond its fundamental transactional attributes.As blockchain can be regarded as an immutable ledger, it can also be exploited in other application scenarios.Ethereum, based on the principles of blockchain, further introduced smart contract technology, allowing any blockchain user to deploy programs on the chain for others to invoke.This has given rise to decentralized applications.Due to the distinct network model of decentralized applications, existing studies regarding the security of centralized models are difficult to directly adapt to decentralized blockchain applications.Therefore, safeguarding information security and user privacy in the blockchain environment has become a focal point of security study.The thesis contains three studies regarding security of decentralized applications.They are primarily categorized into two types according to the scenarios.One type involves using blockchain to enhance existing applications, while the other focuses on native applications based on blockchain.In the first scenario, I utilize blockchain to provide a verifiable endorsement solution for identity authentication in social networking service (SNS) scenarios.Previous identity authentication research in this area rarely incorporated blockchain.I significantly enhance the reliability and efficiency of identity authentication while preventing impersonation using blockchain.In the second scenario, our focus is on NFT trading within the blockchain.Existing research has only addressed NFT privacy issues to a limited extent due to conflicts with Ethereum's transparency principle.To tackle this problem, I proposed two solutions: first, I make the marketplace a semi-trusted entity to achieve anonymous NFT trading.Then, based on previous research, I further propose a solution for anonymous NFT transactions in a trustless environment.Through security analysis and performance evaluation, our solutions meet security requirements with acceptable expenses, making them suitable for practical applications.

Yusong Yao, Yiwen Shi, Guohua Tian, Meixia Miao · 5 authors

No abstract is available for this record.

Cyprien Delpech de Saint Guilhem, Robi Pedersen

No abstract is available for this record.

Subhranil Das, Rashmi Kumari, Raghwendra Kishore Singh, Dev Rishi · 5 authors

Zero Knowledge Proofs (ZKPs) have emerged as transformative cryptographic primitives, enabling a wide variety of modern privacy-preserving applications across various domains. This paper presents substantial advancements in contemporary ZKPs, exploring the latest trends in ZKP schemes, novel applications, and scalable ZKP-based systems. We provide an in-depth analysis of state-of-the-art ZKP structures, including zk-SNARKs, zk-STARKs, and the more recent zk-Rollup technologies. We examine the unique properties and use cases of these techniques, highlighting their potential to enhance privacy, security, and trust in digital systems. Our research into zk-Rollups demonstrates significant improvements in transaction throughput and gas efficiency, paving the way for the extensive deployment of modern privacy-preserving solutions. Furthermore, we showcase several innovative applications of ZKPs, such as privacy-preserving decentralized finance (DeFi) protocols, anonymous credentials, and secure multi-party computation. These use cases illustrate the transformative potential of advanced ZKPs to improve privacy and security across a range of digital domains. To foster wider adoption of modern ZKPs, we have developed and opensourced a collection of advanced tools and libraries that simplify the implementation of ZKP-based solutions. We also offer practical guidance and best practices for developers and researchers working in this field, aiming to accelerate the development and real-world impact of modern zero-knowledge proof systems.

Yang Han

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.

Margarita Capretto, Martín Ceresa, César Sánchez

Abstract Blockchains are decentralized systems that provide trustable execution guarantees through the use of programs called smart contracts. Smart contracts are programs written in domain-specific programming languages running on blockchains that govern how tokens and cryptocurrency are sent and received. Smart contracts can invoke other smart contracts during the execution of transactions initiated by external users. Once deployed, smart contracts running code cannot be modified, so techniques like runtime verification are very appealing for improving their reliability. Moreover, the conventional model of computation of smart contracts is transactional: once operations commit, their effects are permanent and cannot be undone. Therefore, errors in smart contracts may lead to millionaire losses of money. In this paper, we present the concept of future monitors which allows monitors to remain waiting for future transactions to occur before committing or aborting. This is inspired by optimistic rollups, which are modern blockchain implementations that increase efficiency (and reduce cost) by delaying transaction effects. We exploit this delay to propose a model of computation that allows bounded future monitors. We show our monitors correct respect with legacy transactions, how they implement bounded future monitors and how they guarantee progress. We illustrate the use of bounded future monitors by implementing correctly multi-transaction flash loans.

Heera Wali, Vishal Kulkarni, Rajashekar Ganiger, Nalini C. Iyer

This study investigates how blockchain technology can be used in the automotive industry and society, with a focus on addressing privacy concerns and compliance issues. The study investigates the application of privacy-preserving methods, including zero-knowledge proofs and anonymous credentials, to protect vehicle data shared with servers and prevent V2X channel spam. The proposed blockchain ecosystem involves collaborations among stakeholders like governmental bodies and automotive industry participants to ensure user privacy, implementation employs Ethereum[5], circom, and ezkl for practical realization, the study demonstrates the potential of blockchain and [7] Zero Knowledge Proofs in overcoming challenges, promoting a more secure digital environment.

Ganesh Dalabanjan, D. G. Narayan

The increasing complexity of cloud computing has prompted a greater emphasis on protecting the privacy, integrity, and security of data stored and processed in the cloud. Data privacy is safeguarded through access control, but existing models such as Role Based Access Control (RBAC) and Attribute Based Access Control (ABAC) rely on a centralized server. If this server is compromised, it poses significant risks to data security. To address this issue, there is a need for Distributed ABAC (DABAC) system for OpenStack services based on blockchain. The unique features of blockchain enables access control systems which ensures data integrity and privacy. Additionally, blockchain offers a level of transparency for both the resource owner and a user. In this work, we propose a smart contract based ABAC system. We implemented the proposed work using an Ethereum blockchain and OpenStack cloud. Furthermore, we evaluated two consensus algorithms for scalability analysis of DABC mechanism. The results demonstrate that DABAC performs better than ABAC in ensuring fine-grade access control with proof of stake consensus algorithm providing better scalability.

Suman Majumder, Sangram Ray, Dipanwita Sadhukhan, Mou Dasgupta · 6 authors

Radio Frequency Identification (RFID) promotes the fundamental tracking procedure of the Internet of Things (IoT) network due to its autonomous data collection as well as transfer incurring low costs. To overcome the insecure exchange of tracking data and to prevent unauthorized access, parallel dependency RFID grouping-proof protocol is applied by the reader to authenticate tags simultaneously. However, conventional grouping-proof authentication schemes are not sufficient for the memory constraint RFID tags due to the recurrent utilization of a 128-bit PRNG (Pseudo Random Number Generator) function. Alternatively, the existing parallel-dependency grouping-proof schemes are not able to overcome numerous limitations regarding session establishment, efficient key management, and multicast message communication within the specified group. In this research, a lightweight, secure, and efficient communication protocol is proposed to overcome the aforementioned limitations using Elliptic Curve Cryptography (ECC) and Zero-Knowledge property to establish a session key among the participated tags, reader, and remote server. The proposed scheme can work in offline mode. The proposed ECC-based parallel dependency grouping-proof scheme is referred to as ECC-PDGPP which abides by the rules of the EPC class-1 gen-2 (C1 G2) standard of RFID tags. Finally, the proposed protocol is analyzed using a formal random oracle model and simulated using a well-known AVISPA simulation tool that shows the proposed scheme is well protected against all potential security threats.

Christ, Miranda, Baldimtsi, Foteini, Chalkias, Konstantinos Kryptos, Maram, Deepak · 6 authors

Zero-knowledge range proofs (ZKRPs) allow a prover to convince a verifier that a secret value lies in a given interval. ZKRPs have numerous applications: from anonymous credentials and auctions, to confidential transactions in cryptocurrencies. At the same time, a plethora of ZKRP constructions exist in the literature, each with its own trade-offs. In this work, we systematize the knowledge around ZKRPs. We create a classification of existing constructions based on the underlying building techniques, and we summarize their properties. We provide comparisons between schemes both in terms of properties as well as efficiency levels, and construct a guideline to assist in the selection of an appropriate ZKRP for different application requirements. Finally, we discuss a number of interesting open research problems.

Lu Lin, Lingyan Han, Liangmin Wang

No abstract is available for this record.

Rihab Benaich, Saida El Mendili, Youssef Gahi

Blockchain technology is vital in strengthening the security of private information, particularly in the healthcare sector. Its features, such as confidentiality, decentralization, security and privacy, address challenges traditional healthcare systems face, such as phishing, denial of service and identity theft attacks. In this regard, our research paper presents a security solution specifically tailored for healthcare applications. This solution integrates decentralized identity management (DIDs) for identity verification, employs the advanced ChaCha20-Poly1305 encryption algorithm to ensure data confidentiality, and utilizes a token-based mechanism for immutable record keeping. Furthermore, it incorporates a pure proof of stake (PPoS) consensus mechanism to enhance system security while optimizing efficiency. This comprehensive and scalable system showcases improvements in cost effectiveness, time efficiency of an average of 6,5 seconds and overall data protection compared to traditional approaches used in healthcare data security.

Kuznetsov, Oleksandr, Anton Yezhov, Vladyslav Yusiuk, Alexandr Kuznetsov

Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address the scalability challenges in modern blockchain systems. This study proposes a methodology for generating and verifying ZKPs to ensure the computational integrity of cryptographic hashing, specifically focusing on the SHA-256 algorithm. By leveraging the Plonky2 framework, which implements the PLONK protocol with the FRI commitment scheme, we demonstrate the efficiency and scalability of our approach for both random data and real data blocks from the NEAR blockchain. The experimental results show consistent performance across different data sizes and types, with the time required for proof generation and verification remaining within acceptable limits. The generated circuits and proofs maintain manageable sizes, even for real-world data blocks with a large number of transactions. The proposed methodology contributes to the development of secure and trustworthy blockchain systems, where the integrity of computations can be verified without revealing the underlying data. Further research is needed to assess the applicability of the approach to other cryptographic primitives and to evaluate its performance in more complex real-world scenarios.

Mingjie Chen, Yi-Fu Lai, Abel Laval, Laurane Marco · 5 authors

No abstract is available for this record.