This paper introduces a microservice-based architecture to revolutionize how official documents are shared, verified, and stored in digital formats. Addressing the pressing issues of privacy, security, and trust, the proposed solution enables the partial and full sharing of documents while safeguarding sensitive data. The architecture comprises seven core microservices, including a data extractor, document obfuscator, notarization service, and decentralized storage through blockchain and IPFS. By obfuscating designated document fields and utilizing a distributed ledger for notarization, the system ensures both the privacy of users and the transparency required for official verification. Through the combination of state-of-the-art encryption techniques, video-based notarization, and blockchain for immutability, this approach enables secure, scalable, and privacy-conscious document sharing. Detailed guidelines are provided for each microservice, from data extraction and field obfuscation to notarization and decentralized storage. Our approach resolves common issues such as data tampering, unauthorized access, and identity fraud while offering a framework for future innovations in digital notarization.
Commitment schemes are cryptographic schemes that can be applied to zero-knowledge proof construction and blockchain construction. Recently, lattice-based cryptography has been intensively investigated due to the promising potential in quantum cryptography. Accordingly, commitment schemes based on lattice assumptions have been studied for practical applications. Notably, applications often require committing an arbitrary message with low communication costs, so commitment schemes must be satisfied with fewer length restrictions and fewer extensions to the messages. Several studies have been conducted to achieve the problem, including the study published by Baum et al. in 2018. However, the scheme in question still utilizes the message domain for extraneous purposes. We design a length-extension-free commitment scheme ComMWMin which the length of the message string is large relative to the length of the commitment string, improving on the commitment scheme of Baum et al. Furthermore, we prove that the hiding and binding properties of ComMWMare based on the hardness of the decisional search knapsack problem and extended search knapsack problems, respectively. Finally, we evaluate the computation costs of generating commitment value between ours and Baum et al.’s commitment scheme.
Vladimir Popov, Mikhail Krupin, Andrew Gross, Georgi Koreli
New advancements in zero-knowledge proof construction, including improvements in user experience, have made blockchain-based privacy applications more accessible than ever.However, additional measures are required to balance the needs of regulators, the basic privacy rights of users, and the constant threat of bad actors.To address these issues, privacy protocols can introduce features designed to increase transparency, encourage compliance, and prevent illicit use.In this paper, current privacy-preserving methods (privacy pools) are explained along with compliance measures designed to prevent illicit usage.These measures are divided into three broad categories: general restrictions, such as transaction limits, deposit quarantine, and geoblocking; selective disclosure, such as privacy-preserving KYC, proof of innocence, and opt-in reporting; and threat identification and prevention, including AML wallet screening.Each of these methods are described in detail along with examples of three privacypreserving protocols (Hinkal, RAILGUN, and zkBob) which utilize varying combinations of these methodologies to achieve privacy informed by selfregulatory compliance.
<abstract><p>The rapid development of blockchain transactions highlights the importance of privacy protection (including anonymity and confidentiality) and underscores the necessity for auditability. Some schemes, such as PGC and Miniledger, support privacy protection and auditability. However, they only offer incomplete privacy protection (i.e., supporting anonymity or confidentiality exclusively). In response to these issues, we propose a scheme that achieves partial anonymity, confidentiality, auditability, and traceability. By integrating a variant of Pedersen commitments and randomizable signatures, we achieve partial anonymity for users and the auditability of transactions, thereby protecting user privacy under audit conditions. Based on the twisted ElGamal encryption algorithm and specially constructed zero-knowledge proofs, we achieve confidentiality of transaction amounts under legal and regulatory conditions. System test results indicate that this scheme effectively meets the above requirements. The feasibility of this scheme is confirmed through system testing, comparative analysis, and security analysis.</p></abstract>
Omar Ahmed, Charles Gouert, Nektarios Georgios Tsoutsos
Cloud computing has been a prominent technology that allows users to store their data and outsource intensive computations. However, users of cloud services are also concerned about protecting the confidentiality of their data against attacks that can leak sensitive information. Although traditional cryptography can be used to protect static data or data being transmitted over a network, it does not support processing of encrypted data. Homomorphic encryption can be used to allow processing directly on encrypted data, but a dishonest cloud provider can alter the computations performed, thus violating the integrity of the results. To overcome these issues, we propose PEEV (Parse, Encrypt, Execute, Verify), a framework that allows a developer with no background in cryptography to write programs operating on encrypted data, outsource computations to a remote server, and verify the correctness of the computations. The proposed framework relies on homomorphic encryption techniques as well as zero-knowledge proofs to achieve verifiable privacy-preserving computation. It supports practical deployments with low performance overheads and allows developers to express their encrypted programs in a high-level language, abstracting away the complexities of encryption and verification.
Federated Learning (FL) is a machine-learning approach enabling collaborative model training across multiple decentralized edge devices that hold local data samples, all without exchanging these samples. This collaborative process occurs under the supervision of a central server orchestrating the training or via a peer-to-peer network. The significance of FL is particularly pronounced in industries such as healthcare and finance, where data privacy holds paramount importance. However, training a model under the Federated learning setting brings forth several challenges, with one of the most prominent being the heterogeneity of data distribution among the edge devices. The data is typically non-independently and non-identically distributed (non-IID), thereby presenting challenges to model convergence. This report delves into the issues arising from non-IID and heterogeneous data and explores current algorithms designed to address these challenges.
The requirements for large amounts of data have promoted the rapid emergence of an industry for trading data. However, the current one-to-one trading constraints in the existing data trading schemes lead to low security and low efficiency. To tackle the challenges, a novel one-to-many distributed data trading scheme is proposed based on blockchain, which enables a data seller to sell one piece of data to multiple data buyers simultaneously, saving storage resources and computing resources significantly. Firstly, some new smart contracts are devised for two decentralized applications. Then, attribute-based searchable encryption technology is proposed to establish a data circulation scheme that realizes end-to-end encryption of data and ensures data security and highly efficient access. Finally, an inspection mechanism based on zero-knowledge proof and a pricing strategy based on the Stackelberg game are designed to guarantee fairness in trading and maximize revenue. The experiment results show that, in comparison to one-to-one trading, the high efficiency of this data trading scheme gradually emerges as the number of buyers (n) is greater than 2, and the run time is less than 1/10 of the former when n =35. Furthermore, the pricing strategy can enable buyers and sellers to obtain more revenue when$\text {n} \gt 4$.
Apurva K. Vangujar, Buvana Ganesh, Alia Umrani, Paolo Palmieri
This article presents a novel e-voting scheme that combines Group Identity-based Identification (GIBI) with Homomorphic Encryption (HE) based on the discrete logarithmic assumption. The proposed scheme uses the Schnorr-like GIBI scheme for voter identification and authorization using zero-knowledge proofs to ensure the anonymity and eligibility of voters. The voter$\textsf {v}_{i,j}$is granted the authorization to cast a valid vote for a single candidate$\textsf {C}_{k}$. The use of distributed ElGamal provides fairness while the use of partial shares for decryption enables individual and universal verifiability without the need for a central authority. The proposed scheme is secure under various scenarios and robust in the random oracle model. The GIBI-HE scheme offers a promising solution for e-voting, providing a sustainable and accessible environment for voters while supporting the unreusability of votes and protecting the privacy of voters.
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.
Distributed Ledger Technology (DLT) has been contentious since the emergence of blockchain in 2008. Security and compatibility with the personal data rights in the EU General Data Protection Regulation (GDPR) are among the controversies that have erupted. Thus, various studies have concluded that the decentralisation and immutability of DLT conflict with personal data rights. This dissertation illustrates that the DLT can be compatible with the GDPR personal data rights.
Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction, and realizes privacy protection. At the same time, the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price. The smart contract is used to complete the verification and game bargaining process, and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial. Finally, by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations, it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment. The experimental results show that the model improves the credibility and fairness of medical data transactions, maximizes social benefits, encourages more patients and medical institutions to participate in the circulation of medical data, and more fully taps the potential value of medical data.
Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through the robust and decentralized architecture of the Hyperledger Fabric. This study demonstrates the viability and effectiveness of the PHR-NFT framework through performance evaluations focused on transaction latency, throughput, and security. This research has valuable implications for enhancing data privacy and security in healthcare practices and insightful information about blockchain-based healthcare systems.
Our world is, at a point in terms of the environment. The fashioned industrial approach, which heavily relies on centralized systems and resource-intensive computing is no longer sustainable. This document delves into a way to embrace Green Web 3.0, Decentralized AI and Edge Intelligence to drive the industry to-ward a more sustainable future. Green Web 3.0 challenges the energy nature of blockchain technology by utilizing eco-friendly protocols such as Proof of Stake which reduces energy consumption and lessens environmental impact. Similarly, Decentralized AI empowers distributed systems decreasing dependence, on data centers and promoting efficient resource utilization. Building on this foundation Edge Intelligence enables real-time decision making and data processing at the source reducing data transfer and optimizing energy efficiency. The combination of these technologies has the potential to revolutionize industries. Picture smart factories adjusting production in real-time supply chains supported by networks and renewable energy networks managed by intelligent edge devices.
Ensuring robustness against adversarial attacks is imperative for Machine Learning (ML) systems within the critical infrastructures of the Industrial Internet of Things (IIoT). This paper addresses vulnerabilities in IIoT systems, particularly in distributed environments like Federated Learning (FL) by presenting a resilient framework - Secure Federated Learning (SFL) specifically designed to mitigate data and model poisoning, as well as Sybil attacks within these networks. Sybil attacks, involving the creation of multiple fake identities, and poisoning attacks significantly compromise the integrity and reliability of ML models in FL environments. Our SFL framework leverages a Digital Twin (DT) as a critical aggregation checkpoint to counteract data and model poisoning attacks in IIoT’s distributed settings. The DT serves as a protective mechanism during the model update aggregation phase, substantially enhancing the system’s resilience. To further secure IIoT infrastructures, SFL employs blockchain-based Non-Fungible Tokens (NFTs) to authenticate participant identities, effectively preventing Sybil attacks by ensuring traceability and accountability among distributed nodes. Experimental evaluation within IIoT scenarios demonstrates that SFL substantially enhances defensive capabilities, maintaining the integrity and robustness of model learning. Comparative results reveal that the SFL framework, when applied to IIoT federated environments, achieves a commendable 97% accuracy, outperforming conventional FL approaches. SFL also demonstrates a remarkable reduction in loss rate, recording just 0.07 compared to the 0.14 loss rate experienced by standard FL systems. These findings highlight the efficiency and applicability of the SFL framework in enhancing data security and traceability within the IIoT ecosystem.
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.
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.
In this paper, a novel Elliptic Crypt with Secured Blockchain-backed Federated Q-Learning Framework is proposed to offer an intelligent healthcare system that mitigates the attacks and data misused by malicious intruders. Initially, the entered IoMT data is collected from publicly available datasets and encrypted using the Extended Elliptic Curve Cryptography (E_ECurCrypt) technique for ensuring the security. This encrypted data is fed as an input to the blockchain-powered collaborative learning model. Here, the federated Q-learning model trains the inputs and analyzes the presented attacks to ensure better privacy protection. Afterwards, the data is securely stored in decentralized blockchain technology. Subsequently, an effective Delegated Proof of Stake (Del_PoS) consensus algorithm is used to validate the proposed framework. The experiment is conducted using the WUSTL-EHMS-2020 dataset and the performances are analyzed by evaluating multiple matrices and compared to other existing methods. The performance of the proposed framework can be assessed using multiple matrices and the results will be compared to other existing methods. As a result, the proposed method has achieved 99.23% accuracy, 98.42% precision, 98.12% recall, 98.27% F1 score, 59080.506 average throughput, 59080.506 average decryption time 1.94 seconds and an average encryption time of 1.84 seconds and are superior to conventional methods.
Today, educational data, controlled centrally by educational institutions and administrative units, may be vulnerable to damage caused by natural disasters, political instability, and wars. Simultaneously, challenges arise in accessing this data for educational activities within the framework of exchange programs or lifelong learning. In the literature, there are numerous blockchain-based studies focusing on storing and sharing data in various fields. While several studies exist on blockchain applications for certification, verification, and data sharing in the education sector, a fully decentralized infrastructure has not yet been presented. To address this issue, it is proposed that data control should shift to the hands of students, who are the rightful owners of the data, rather than being solely in the hands of educational institutions. In alignment with the decentralized internet vision, Web3, public blockchain networks are considered the most suitable infrastructure for this purpose. To meet this need, a framework named PublicEduChain has been introduced within the scope of this study. PublicEduChain allows students to store their data in smart contracts created on the public Ethereum network, making it possible to share this information with any educational institution and administrative units. Educational institutions can access student data stored in smart contracts on the public Ethereum network through Learning Management System (LMS) applications and can add data to these contracts. PublicEduChain ensures that data is managed under student ownership within a fully decentralized infrastructure. The practical steps in PublicEduChain, such as creating a smart contract, logging into LMSs with Ethereum IDs, and allowing LMSs to read and write data in the student contract, are explained in detail.