Decentralized storage platforms based on consortium blockchains have emerged in the spotlight of research and industry communities because they are flexible, transparent, and eliminated trust in contrast to the traditional centralized data-sharing model. However, due to wide attacking surfaces in a blockchain network, this decentralized data-sharing paradigm is subject to malicious data breaches. Untrusted blockchain nodes can directly obtain sensitive information from the query processing and their local storage. Several studies have been made for solving this dilemma, but they only focus on single-user settings and cannot be directly applied to multi-owners blockchain-based data sharing scenarios. In this paper, we introduce FedEDB, a federated and encrypted data store by using consortium blockchains. Unlike existing solutions that focus on single-user settings, our proposed schemes can efficiently support privacy-preserving and reliable multi-owner queries in the decentralized setting. We start from the practical key aggregation technique to construct the multi-owner search schemes and further refine the underling building blocks to enhance the security. Besides, we integrate the smart contract with our tailored zero-knowledge proof to enforce secure and reliable result verification protocol with fairness. We implement a prototype and thorough security analysis and comprehensive evaluation results confirm the practicability of our design.
Cross-border data privacy protection often involves personal privacy data from different regions, where cross-border vehicle identity authentication requires a large amount of sensitive data. The cross-border movement of this sensitive data poses a significant threat to privacy. A distributed identity management blockchain model for cross-border data privacy protection is proposed to avoid the cross-border transmission of sensitive data through identity authentication. The model combines the SM2 and SM9 algorithms and blockchain technology to guarantee the security of stored data while providing a method to avoid sensitive data crossing borders and realizing cross-border identity authentication. The model was originally designed for the Northbound Travel for Macao scenario but can still be applied to other cross-border authentications. The generation speed of a Non-Fungible Token is verified through experiments, and the generation time and efficiency of Non-Fungible Tokens satisfy the actual needs of Internet of Vehicles authentication.
The implementation of blockchain technology alongside Artificial Intelligence features that strengthen Internet of Things cloud-based systems through extended data protection, enhanced robotic trust, and decentralized intelligence capabilities.Both potential benefits and obstacles of building blockchain-empowered collaborative AI systems that perform secure computations across multiple parties and present architectural guidelines for privacy protection.Digital transformation now drives various industries forward because of the power combination between IoT and distributed ledger technology and their alignment with AI and edge-fog-cloud computing environments.Blockchain integration with IoT networks protects data integrity by remedying vital privacy and security problems, which creates a robust system that handles decentralized, secure data management.Blockchain technology makes financial operations secure and faster across all payment transactions, trade finance, and asset management operations to build complete trust with banking institutions.Through their mutual partnership, blockchain and robotic technologies develop advanced robotic systems that exhibit better operational performance and use strengthened security systems to address blockchain weaknesses.This leads to better dependability of AIdriven service operations.Multiple forces drive blockchain integration with AI applications because users need stronger data security basics to protect confidential data from unauthorized use or tampering, and they want more reliable robot decision authentication.
Federated Learning is susceptible to various kinds of attacks like Data Poisoning, Model Poisoning and Man in the Middle attack. We perceive Federated Learning as a hierarchical structure, a federation of nodes with validators as the head. The process of validation is done through consensus by employing Novelty Detection and Snowball protocol, to identify valuable and relevant updates while filtering out potentially malicious or irrelevant updates, thus preventing Model Poisoning attacks. The opinion of the validators is recorded in blockchain and trust score is calculated. In case of lack of consensus, trust score is used to determine the impact of validators on the global model. A hyperparameter is introduced to guide the model generation process, either to rely on consensus or on trust score. This approach ensures transparency and reliability in the aggregation process and allows the global model to benefit from insights of most trusted nodes. In the training phase, the combination of IPFS , PGP encryption provides : a) secure and decentralized storage b) mitigates single point of failure making this system reliable and c) resilient against man in the middle attack. The system is realized by implementing in python and Foundry for smart contract development. Global Model is tested against data poisoning by flipping the labels and by introducing malicious nodes. Results found to be similar to that of Flower.
Blockchain technology is increasingly being used in personal data protection. Inspired by the importance of data security, this paper proposes a personal data protection mechanism based on blockchain, combined with distributed hash tables and cryptography, to enhance users' control over the data generated using web applications. This paper designs this mechanism's system model and describes the three aspects in detail: data storage mechanism, data encryption mechanism, and data trading mechanism. Among them, the data storage mechanism restricts user data to be stored only in the local storage space of the user terminal, the decentralized blockchain network, and the distributed hash table network to ensure that enterprises providing network applications cannot privately store user interaction data, the encryption mechanism is responsible for encrypting all user data recorded in the network and allows users to control the key of the data to ensure the security of the user data in the blockchain and distributed hash tables, the data transaction mechanism allows users to trade their data, and to incentivize enterprises to assist users in collecting personal data, data transaction contracts are built into the data transaction mechanism, allowing enterprises to receive a share of the revenue from user data transactions. Then, for data transactions, use the Stackelberg game to simulate the revenue sharing between users and service providers in data trading to incentivize enterprises providing web services to assist users in collecting their data. The simulation results show that when the number of users is 1000, the revenues of this scheme for service providers are 31%, 561%, and 19% higher than the existing scheme. Finally, the personal data protection platform is implemented by code to verify the feasibility of the theory proposed in this paper in personal data protection.
Fengqun Wang, Jie Cui, Qingyang Zhang, Debiao He · 6 authors
In the Industrial Internet of Things (IIoT), data sharing is crucial for promoting the intelligent development of industrial production. To achieve effective data supervision, introducing blockchain into traditional cloud-based data-sharing frameworks has attracted widespread attention. However, existing blockchain-based data-sharing schemes still have issues with security and efficiency. Therefore, we propose a blockchain-enabled data-sharing scheme based on proxy re-encryption. First, the scheme considers both storage and access authentication, guaranteeing data sources’ trustworthiness and preventing data misuse. Second, the scheme uses an on-chain and off-chain cooperative storage mechanism, saving the storage resources of the blockchain. Third, the scheme supports data packing, which effectively improves data storage efficiency. The security analysis shows that our scheme satisfies the security requirements. Finally, we build a blockchain platform using the hyperledger fabric. The performance evaluation shows that our scheme is more advantageous regarding computational overhead than other related schemes.
Currently, India is marching towards complete e-Governance. In healthcare, the NHA (National Health Authority) initiated the ABDM (Ayushman Bharat Digital Mission) to assist the country's integrated digital health infrastructure. This mission's main focus is on creating a national digital health ecosystem. The main aim of this ecosystem is to facilitate a trustworthy, easy-to-share environment for storing health records. To speed up the national digital health ecosystem's development, the NHA is looking forward to creating innovative solutions and inviting interested parties to participate in the development. Data security and privacy play a vital role in this situation. With its distributed, immutable ledger property, blockchain offers a promising result. Many countries are trying to adopt blockchain for their health applications, like patient data storage, maintaining drug supply chains, and insurance claims. In this paper, we identified some solutions using blockchain platforms and their usage and benefits to establish a secure, transparent, and trustworthy environment in India.
This paper proposes a novel approach to securing electronic health records(EHRs) using non-fungible tokens (NFTs). NFTs are unique digital tokens that can be used to represent ownership or proof of authenticity of digital assets. The proposed framework explores the use of NFTs for secure patient data storage, secure data transfer, data provenance, and patient ownership of data. By using NFTs to establish secure and auditable records of patient data, electronic health records management systems can better protect patient privacy and security. The paper also discusses the challenges and opportunities of using NFTs in EHRs, as well as the legal and ethical implications of giving patients ownership and control over their medical data. This paper presents a new avenue for research into securing EHRs using NFTs, which has the potential to improve patient outcomes and advance the field of health informatics.
The primitive of verifiable data streaming (VDS) provides a secure data outsourcing solution for resource-constrained users, that is, they can stream their continuously-generated data items to untrusted servers while enabling publicly verifiable query and update. However, existing VDS schemes either require the server to store the authentication tags of all data items to support data query and auditing, or bind all data items into a constant-size tag to achieve optimal storage on the server side, but cannot achieve public auditing. To close this gap, in this paper, we first design a novel authentication data structure, dubbed retrievable homomorphic verifiable tags (RHVTs), which allows users to aggregate the authentication tags of all data items into a constant-size tag, and enables them to retrieve the original tags from the aggregated tag when necessary. Based on this, we propose a compact verifiable and auditable data streaming (CVADS) scheme, which adopts a single-level authentication mechanism to achieve more efficient data append and update, as well as optimal storage and public auditing. For better robustness and performance, we introduce a nested dual-level authentication mechanism and propose a blockchain-based CVADS (BCVADS) scheme to achieve a distributed CVADS with self-auditing. Finally, we prove the security of our schemes in the random oracle model and demonstrate their practicality through a visual performance evaluation.
Mobile crowdsourcing (MCS) is an emerging paradigm that enables the outsourcing of a complex task to a group of mobile devices. The ability to utilize the collective power of mobile devices and human intelligence makes MCS a significant tool in various scenarios. Nevertheless, it faces practical challenge in protecting user privacy due to the sensitive nature of information collected by mobile devices. Additionally, the inherent openness of MSC and the heterogeneity of mobile devices raise reliability concerns among participants. To address these challenges, by integrating Federated Learning with the pairwise additive masking technique and the Chinese Remainder Theorem, we propose a Blockchain-based Privacy-preserving Federated Learning (BPFL) framework for mobile crowdsourcing, which allows mobile participants to collaboratively solve a crowdsourced machine learning task while preserving privacy. Besides, it employs blockchain technology to record the training process in a transparent and tamper-proof ledger. This ledger guarantees the verifiability of aggregation results and the fair distribution of training rewards, thereby enhancing trust and fairness. We prove that our BPFL supports privacy protection and trust mechanism simultaneously and resists inference and collusion attacks. Experimental results show that our BPFL can achieve high performance in terms of computation cost, communication cost and model accuracy, which is friendly for mobile users with resource-constrained devices in MCS ecosystems.
In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud.
Minh-Quan Le, Hai‐Duong Le, Anh Vu Dinh-Duc, Thanh-Tung Tran
Recent blockchain-based systems for managing credentials show advantages over paper-based procedures. However, issuing credentials with blockchain could conflict with current management rules and policies. One of the possible conflicts is the auditability. Most blockchain-based systems for credentials focus on security, efficiency and privacy while ignoring the auditability of the system. In this paper, we propose a new system IU-TransCert for issuing, verifying, and auditing academic credentials. The system uses a new data structure named the Auditable Merkle Tree that enables credential issuance and built-in auditing capabilities. The auditable data fields can be customized to meet regulations. Credentials are published to the blockchain in the root node of the Auditable Merkle Tree, allowing access for auditors while preserving privacy. The system provides automated and transparent auditing processes for educational authorities to independently verify credentials without involving issuers. We also present a prototype to demonstrate feasibility, and a security analysis to examine protections against threats. The analysis and discussion shows that the proposed system could enhance credential privacy, efficiency, integrity, and auditability across the university ecosystem.
Ralph Ankele, Celi, Sofia, Giles, Ralph, Haddadi, Hamed
In the era of data-driven economies, incentive systems and loyalty programs, have become ubiquitous in various sectors, including advertising, retail, travel, and financial services. While these systems offer advantages for both users and companies, they necessitate the transfer and analysis of substantial amounts of sensitive data. Privacy concerns have become increasingly pertinent, necessitating the development of privacy-preserving incentive protocols. Despite the rising demand for secure and decentralised systems, the existing landscape lacks a comprehensive solution. In this work, we propose the BOOMERANG protocol, a novel decentralised privacy-preserving incentive protocol that leverages cryptographic black box accumulators to securely and privately store user interactions within the incentive system. Moreover, the protocol employs zero-knowledge proofs to transparently compute rewards for users, ensuring verifiability while preserving their privacy. To further enhance public verifiability and transparency, we utilise a smart contract on a Layer 1 blockchain to verify these zero-knowledge proofs. The careful combination of black box accumulators and zero-knowledge proofs makes the BOOMERANG protocol highly efficient.
With the continuous development of Internet of vehicles (IoV) services and the continuous influx of network users, how to solve the security and user privacy protection problems of IoV system has become a top priority. In this regard, based on the in-depth analysis and research of the existing cryptographic theory and anonymous authentication scheme, aiming at the key issues in the security architecture of IoV based on blockchain technology, we propose an anonymous authentication scheme for IoV based on private blockchain. We design a private blockchain-based anonymous authentication scheme for IoV. With the help of the non-tampering characteristics of blockchain, through the decentralized and distributed public key infrastructure (PKI), the registration pressure under high load is effectively shared. In addition, based on the system architecture of distributed authentication, we also design the anonymous identity update stage of the vehicle through the form of hybrid chain, which realizes the continuous tracking and tamper resistance of the vehicle anonymous identity binding information. Finally, through the security analysis and the implementation of the simulation system, we prove that the scheme meets the security requirements and effectively improves the efficiency of the anonymous authentication process.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In recent years, mobile crowdsensing has emerged as a key technology for digital city construction, which acquires sensing data and mining data values with low cost and high flexibility. However, limited by the centrality of the sensing system, the complexity of the sensing process, and the uncertainty of sensing users, mobile crowdsensing has been severely suffering downgrading problems of security reliability and quality of service (QoS). In this study, a two-stage game involving blockchain technique is proposed to deal with security and QoS problems in mobile crowdsensing. First, a blockchain-based mobile crowdsensing system with a dual currency mechanism is established in a distributed manner to ensure the reliability of the sensing system. Second, a consensus protocol is designed to strengthen the security of sensing data based on a credit-based verifiers declaration game. Third, a quality rating mechanism based on the workers attitude game is established to improve the service quality of sensing tasks. Finally, some numerical results demonstrate the secure and efficient performances of mobile crowdsensing with the proposed framework.
Cryptographic access control guarantees that authorized users can access data while unauthorized get nothing. Such an all-or-nothing access mode achieves secrecy but does not fit strong-privacy scenarios. FE-based access control breaks it and reaches a balance between data privacy and data utilization. To resist malicious senders, Damgard et al. introduced sanitizable functional encryption that enables a bi-directional control to both senders and receivers. However, its centralized structure means that the compromise of the authority incurs massive secret leakage and undermines the system’s reliability. In this work, we present SanIdea, a sanitizable, decentralized and privacy-preserving access control framework which embraces a sanitizer in the distributed-authority-domain access control setting. We instantiate it by proposing a cryptographic primitive named sMABE, which adds a$\mathsf {Sanitize}$algorithm over multi-authority attribute-based encryption. We formally prove its security in the IND-CPA model and the Sanitization Security model under the DBDH assumption. We demonstrate its reasonable efficiency through algorithm simulation, where the sanitization time is less than 0.1s with the configuration of 5 attribute authorities and 25 user attributes. We design an SABC system by integrating SanIdea with the blockchain, where SABC uses a smart contract to ensure the correctness of the distributed secret key parts. We implement SABC in an Ethereum testbed and the experiment results show that the$\mathsf {upload}$algorithm costs about 163000 user gas and the$\mathsf {download}$algorithm costs about 84000 user gas, which is cost-reasonable.
Chuangming Zhou, Yang Zhou, Shaohua Yue, Bona Xuan · 5 authors
In the era of big data, facilitating efficient data flow is of paramount importance. Governments and enterprises worldwide have been investing in the big data industry, promoting data sharing and trading. However, existing data trading platforms often suffer from issues like privacy breaches, single points of failure, data tampering, and non-transparent transactions due to their reliance on centralized servers. To address these challenges, blockchain-based big data transaction models have been proposed. However, these models often lack system integrity and fail to fully meet user requirements while ensuring adequate security. To overcome these limitations, this paper presents an Ethereum-based big data trading model that establishes a comprehensive and secure trading system. The model aims to provide users with more convenient, secure, and professional services. Through the utilization of smart contracts, users can efficiently match data and negotiate prices online while ensuring secure data delivery through encryption technologies. Additionally, the model introduces a trusted third-party entity that offers professional data evaluation services and actively safeguards user data ownership in the event of disputes. The implementation of the model includes the development of smart contracts and the necessary machine learning code, followed by rigorous testing and validation. The experimental results validate the effectiveness and reliability of our proposed model, demonstrating its potential to ensure effective and secure big data trading.
In recent years, recommendation systems have gained popularity in the financial services industry. These systems offer personalized recommendations to consumers based on their distinct preferences, behaviors, and historical data. Centralized data storage and processing used in traditional recommendation systems can raise privacy and security concerns. In light of these challenges, in this paper, a federated learning-empowered recommendation model (FLRM) is proposed that utilizes federated learning and blockchain technology. In our proposed model, the central server coordinates model aggregation and communicates with the blockchain network. In FLRM, financial institutions hold their data in private blockchains while participating in the federated learning process. Federated learning provides a solution to these challenges by enabling privacy-preserving collaborative model training across multiple distributed data sources. Blockchain technology enhances the security and transparency of the federated learning model by providing a decentralized and tamper-proof mechanism for data storage and management. By decentralizing the recommendation system, FLRM enhances user privacy, reduces data transfer overhead, and builds trust through transparency. The integration of smart contracts in this proposed model facilitates secure and automated transactions. The proposed approach represents a significant step forward in creating a more secure, privacy-preserving, and effective recommendation system model.
Healthcare has moved towards integrating wireless technology to create and enable more services for the patients' convenience. Among the new and popular services is the formulation of wearable Internet of Things (IoT)-enabled telemedicine. However, when the number of IoT devices entering the network increases, it creates concerns about the service's ability to keep its data secure and preserve its real-time capabilities. To address these issues, in this work, we propose a fog-based IoT platform incorporating blockchain technology and smart contracts. We evaluated our design's ability to keep the real-time capabilities of wearable IoT-enabled telemedicine services through experimentation. According to the results, the introduced platform can effectively reduce the overall latency of data transactions compared to other standard network configurations. We further evaluated and observed the contributions of our design to the security of the telemedicine service. Through experimentation, we prove the feasibility of the proposed platform in addressing the highlighted issues of wearable IoT-enabled telemedicine services.
To improve the security of crowdsourcing, existing studies introduce blockchain to ensure reliability and utilize cryptography (e.g., encryption and zero-knowledge proof) to protect data privacy. Nevertheless, the crowdsourcing process may involve sensitive identity information, and identity protection remains unresolved during stages such as data submission and correct payment. Especially, when workers invoke a smart contract to submit data, it inevitably exposes their identities. Identity disclosure significantly impacts the credibility of crowdsourcing platforms. Existing solutions suggest solving the problem through anonymous token contracts such as Zether. However, tokens can easily result in fund freezing or extra information leakage. Moreover, invoking the contract to submit data will still disclose workers' blockchain accounts. To tackle the identity protection issue, in this paper, we propose AcCrowd which achieves worker anonymity and payment correctness in crowdsourcing systems atop blockchain. We first design a verifiable proxy submission mechanism for data submission, enabling workers to invoke contracts without disclosing their accounts. Then, we introduce and improve the BlockMaze architecture to replace previous anonymous token-based methods, enhancing privacy and flexibility. Besides, we designed a revealed payment mechanism that utilizes an adaptor signature to bind data reveal and reward payment together, simultaneously protecting the requester and worker. Our security and performance evaluations demonstrate the security and practicability of AcCrowd.
The advancing fifth-generation technology has brought the Internet of Vehicles (IoV) more and more attention. The vast amount of data collected and generated by vehicles has enabled enhanced driving security and intelligent transportation through data sharing between vehicles and roadside infrastructures. Due to the huge data volume and vehicle resource constraints, vehicular edge networks have been proposed to provide computational and storage resources. Vehicular data commonly contains sensitive information about geographical locations and personal identifiers, making it vulnerable to attacks and leaks. In this paper, we propose a privacy-preserving framework for blockchain-enabled data sharing in vehicular edge networks. Attribute-based ring signatures and threshold proxy re-encryption are used to address privacy concerns, and a decentralized regulatory approach has been proposed to track illegal data transfers. We address the security and trust issues by using the consortium blockchain as a distributed ledger of correlated information records. We also implement consent-based access control for data owners, which is rarely considered in currently proposed systems. Implementation and experiments show that our scheme can handle large throughput and data sizes. The security analysis shows the promise of our scheme in ensuring security against various attacks.