Blockchain-Based Privacy-Preserving Research Articles

Blockchain-Based Privacy-Preserving Federated Learning for Mobile Crowdsourcing

Blockchain-Based Privacy-Preserving Federated Learning for Mobile Crowdsourcing

AnonymousTollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

AnonymousTollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

Retracted: SmartMedChain: A Blockchain-Based Privacy-Preserving Smart Healthcare Framework.

[This retracts the article DOI: 10.1155/2021/4145512.].

A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT

The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.

Go-Sharing: A Blockchain-Based Privacy-Preserving Framework for Cross-Social Network Photo Sharing

The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. In this paper, we propose Go-sharing, a blockchain-based privacy-preserving framework that provides powerful dissemination control for cross-SNP photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, our framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols. We use these protocols to create platform-free dissemination trees for every image, providing users with complete sharing control and privacy protection. Considering the possible privacy conflicts between owners and subsequent re-posters in cross-SNP sharing, we design a dynamic privacy policy generation algorithm that maximizes the flexibility of re-posters without violating formers' privacy. Moreover, Go-sharing also provides robust photo ownership identification mechanisms to avoid illegal reprinting. It introduces a random noise black box in a two-stage separable deep learning process to improve robustness against unpredictable manipulations. Through extensive real-world simulations, the results demonstrate the capability and effectiveness of the framework across a number of performance metrics.

Blockchain-Based Privacy-Preserving Sensor Data Sharing with Fine-Grained Authorization in Microgrid

Microgrid is a power system that includes various energy sources (e.g., solar panels and wind turbines), where a number of device status and sensing data are collected and transmitted by smart sensors. Based on sensing-as-a-service in microgrid, sensor owners and sensor data consumers can effectively perform data sharing operations. However, the state-of-the-art sensor data sharing works in microgrid have the following two limitations: (i) cannot support fine-grained authorization for sensor owners and sensor data consumers and (ii) fail to simultaneously consider confidentiality and authenticity for sensor data sharing. To address the problems, in this article, we propose a lightweight privacy-preserving sensing data sharing system with fine-grained authorization in microgrid. Technically, we employed attribute-based signature methodology to design a fined-grained authorization mechanism for sensor data users. Moreover, a lightweight hyper elliptic curve-based signcryption scheme is employed to provide confidentiality and authenticity for sensor data sharing. To clarify the feasibility of our proposed system, we implement the system and evaluate the performance. The experimental results show that the system achieves small communication and time overhead, as well as highly acceptable gas consumption of smart contract.

A blockchain-based privacy-preserving and collusion-resistant scheme (PPCR) for double auctions

Electronic auctions (e-auctions) remove the physical limitations of traditional auctions and bring this mechanism to the general public. However, most e-auction schemes involve a trusted auctioneer, which is not always credible in practice. Some studies have applied cryptography tools to solve this problem by distributing trust, but they ignore the existence of collusion. In this paper, a blockchain-based Privacy-Preserving and Collusion-Resistant scheme (PPCR) for double auctions is proposed by employing both cryptography and blockchain technology, which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy. A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance. A Dispute Resolution agreement (DR) is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and correct. In addition, a Concise Dispute Resolution protocol (CDR) is designed to handle situations where the number of accused winners is small, significantly reducing the computation cost of dispute resolution. Extensive experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.

SecureMed: A Blockchain-Based Privacy-Preserving Framework for Internet of Medical Things

The Internet of Medical Things (IoMT) connects a huge amount of smart sensors with the Internet for healthcare service provisioning. IoMT’s privacy-preserving becomes a challenge considering the life-saving data collected and transferred through IoMT. Traditional privacy protection techniques use centralized management strategies, which lead to a single point of failure, lack of trust, state modification, information disclosure, and identity theft. Edge computing enables local computation of IoMT data, which reduces traffic to the cloud and also helps in accomplishing latency-sensitive healthcare applications and services. This paper proposes a novel framework (i.e., SecureMed) that uses blockchain-based distributed authentication implemented at the edge cloudlets to enforce privacy protection. In SecureMed, IoMT devices interact with edge cloudlets using smart contracts. It uses trusted edge nodes to implement an authentication algorithm that uses public/private key matching to authenticate IoMT. Experimental evaluation performed using the Pythereum blockchain shows that SecureMed outperforms the traditional blockchain scheme based on latency, bandwidth consumption, deployment time, scalability, and accuracy. Therefore, it can be used to protect the edge-enabled IoMT from privacy attacks and to ensure end-to-end healthcare service provisioning.

Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

Cloud storage has been widely used to team work or cooperation development. Data owners set up groups, generating and uploading their data to cloud storage, while other users in the groups download and make use of it, which is called group data sharing. As all kinds of cloud service, data group sharing also suffers from hardware/software failures and human errors. Provable Data Possession (PDP) schemes are proposed to check the integrity of data stored in cloud without downloading. However, there are still some unmet needs lying in auditing group shared data. Researchers propose four issues necessary for a secure group shared data auditing: public verification, identity privacy, collusion attack resistance and traceability. However, none of the published work has succeeded in achieving all of these properties so far. In this paper, we propose a novel blockchain-based ring signature PDP scheme for group shared data, with an instance deployed on a cloud server. We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature (LHARS) to implement public anonymous auditing for group data. We also build smart contracts to resist collusion attack in group auditing. The security analysis and performance evaluation prove that our scheme is both secure and efficient.

Blockchain-Based Privacy-Preserving and Rewarding Private Data Sharing for IoT

The Internet of Things (IoT) devices possessed by individuals produce massive amounts of data. The private data onto specific IoT devices can be combined with intelligent platform to provide help for future research and prediction. As an important digital asset, individuals can sell private data to get rewards. Problems, such as privacy, security, and access control prevent individuals from sharing their private data. The blockchain technology is widely used to build an anonymous trading system. In this article, we construct a blockchain-based privacy-preserving and rewarding private data-sharing scheme (BPRPDS) for IoT. A privacy issue worth considering is that the malicious cloud server may establish a behavior profile database of data users (DUs). In the case of anonymity, the transactions of private data sharing are easy to cause disputes. When anonymous DUs are framed, it is hard to protect their rights. With the help of the deniable ring signature and Monero, we realize the behavior profile building prevention and nonframeability of BPRPDS. At the same time, we utilize the licensing technology executed by smart contracts to ensure flexible access control of multisharing. The proposed BPRPDS is provably secure. Performance analysis and experimental results show that BPRPDS is efficient and practical.

Blockchain-Based Privacy-Preserving Approach Using SVML for Encrypted Smart City Data in the Era of IR 4.0

The collection of amounts of useful data from various IoT devices through machine learning (ML) techniques has been extensively applied in lots of areas of the smart city. To improve the efficiency of machine learning, people realize efficient data classification by supporting the ML model, namely, the support vector machine (SVM) model, which is used in the realization of linear classification. However, data security and data protection are not addressed in SVM classifier training from multiple entity-labeled IoT data. In the existing solution, they depend on the implicit hypothesis that learning data can be dependable collected from complex data providers, which is often not the reality. To solve the question between the above problems, in this article, we put forward a secure support vector machine—a secure SECURE SVM training scheme for protecting the privacy on encrypted IoT data based on blockchain. Firstly, a secure and dependable data encryption sharing platform is established among complex data providers by using blockchain technology, which is recorded in a distributed ledger. Secondly, the homomorphic cryptosystem is used to design secure components, and the secure polynomial multiplication is improved to design a secure SVM training algorithm, which only needs two interactions in one iteration and does not need a trusted third party. Finally, the experimental results display that the plan ensures the confidentiality of sensitive data of each data provider and the confidentiality and validity of SVM model parameters of data analysts.

A model for blockchain-based privacy-preserving for big data users on the internet of thing

Recently, with the emergence and growth of the IoT as a promising vehicle for sustainable development, the concept of ‘smart cities’ has advanced significantly. However, many challenges inhibit the development of using IoT applications in smart cities, such as issues of privacy, scalability, trust, security, and centralisation. On a daily basis in smart cities, the IoT generates a large amount of data (big data) which could potentially be used for questionable or suspect purposes by attackers. The weight of the security issues surrounding big data must be acknowledged as the associated technology is continuously developing. To solve this issue, a strategy that secures important and potentially sensitive user information on a distributed blockchain and transmits non-sensitive information to the primary system by controlling the size of the blockchain is proposed. This solution cannot be achieved in traditional blockchain because it requires too many resources. The model is composed of three proposed algorithms: the first aims to allocate data to each user; the second performs the process of searching for data, and the third confirms the communication process. Experiments have proved that this proposed protocol for blockchain has excellent byzantine fault tolerance. The final experimental results of the proposed model established that the algorithms effectively meet the performance requirements.

A Blockchain-based privacy preserving mechanism for mobile crowdsensing

In blockchain-based mobile crowdsensing, reporting of real-time data is stored on a public blockchain in which the address of every user/node is public. Now, the problem lies in the fact that if their addresses get shown to adversaries, all their transactions history is also going to be revealed. Therefore, crowdsensing demands a little privacy preservation strategy in which the identity of a user is unable to be revealed to an adversary or we can say that crowd sensors while reporting the real-time data must provide some level of anonymity to crowdsensing users/nodes [1]. The current crowdsensing architecture is not secure because of its centralized nature and the reason is a single point of failure also numerous kinds of attacks are possible by adversaries such as linkage attacks, Sybil attacks, and DDOS attacks to get the identity or any other valuable information about the nodes. The location of crowd sensors is also a threat that could lead to adversarial attacks. Consequently, some blockchain-based models must be proposed to attain privacy on the blockchain ledger. The solution can either be made up crowdsensing environment on a private blockchain or smart contracts may be the answer to this problem by which we can make the users secure from several attacks conducted by adversaries on the blockchain.

Blockchain-Based Privacy Preserving and Energy Saving Mechanism for Electricity Prosumers

With the development of distributed and renewable energy resources and smart grids, energy management systems that allow electricity prosumers to schedule their power usage, are seen as a prominent solution for reducing electricity costs. This paper presents a novel blockchain-based mechanism to incentivize prosumers to save energy, while preserving their privacy. In the proposed mechanism, each prosumer utilizes an energy management system that is based on the percentage power change (PPC) at each hour of the day. The use of PPC values allows the proposed blockchain to preserve the privacy of the prosumers as no sensitive information is shared. The calculated PPC values are shared among the prosumers. The prosumer with the minimum PPC value is selected as the validator of the blockchain, which is responsible for creating the next block of the blockchain. The problem of approving the validator, by other prosumers, is formulated using a novel zero-metric weighted average consensus. The communication model required to reach this consensus is investigated and the consensus value is analytically derived. Multiple test systems with varying number of prosumers are simulated and analyzed. The results demonstrate the capability of the proposed mechanism to sustain energy while preserving privacy in a scalable manner.

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

Blockchain-Based Privacy-Preserving Driver Monitoring for MaaS in the Vehicular IoT

Driving behaviors are highly relevant to automotive statuses and on-board safety, which offer compelling shreds of evidence for mobility as a service (MaaS) providers to develop personalized rental prices and insurance products. However, the direct dissemination of driving behaviors may lead to violations of identity and location privacy. In this paper, our proposed mechanism first achieves the verifiable aggregation and immutable dissemination of performance records by exploiting a blockchain with the proof-of-stake (PoS) consensus. Moreover, to acquire a driver's aggregated performance record from the blockchain, the proposed scheme first realizes quick identification with a Bloom filter and further approaches the target performance record through an oblivious transfer (OT) protocol. A performance evaluation shows that during the acquisition of the records, the computational complexity of our scheme is only related to the scale of the records contained in one transaction. However, the computational complexity of one traditional scheme without a Bloom filter depends on the scale of the records generated during each time slot. Furthermore, the computational complexity of another traditional scheme without aggregation relies on the scale of the records contained in one transaction, as well as the length of a driver's performance history. We also investigate the trade-off between the privacy level and computational complexity, and we determine the optimal number of data records in each transaction.

Proof of Pseudonym: Blockchain-Based Privacy Preserving Protocol for Intelligent Transport System

Proof of Pseudonym: Blockchain-Based Privacy Preserving Protocol for Intelligent Transport System

MEXchange: A Privacy-Preserving Blockchain-Based Framework for Health Information Exchange Using Ring Signature and Stealth Address

Health information exchange (HIE) refers to the integrated management and secure sharing of health information among healthcare entities. HIE improves healthcare quality and streamline healthcare administrative work. These advantages have propelled health-care stakeholders to implement HIE. However, challenged by issues such as security, privacy, and costs, HIE is not widespread. Recent studies have suggested blockchain-based HIE for solving security and privacy issues. Unfortunately, existing blockchain-based HIE studies do not consider the privacy issues caused by analyzing senders and receivers of transactions in the blockchain. In this work, we suggest MEXchange, a novel blockchain-based privacypreserving HIE that prevents the privacy issue by obscuring the sender and concealing receiver addresses. We propose smart contracts and workflow that use ring signature and stealth address for blockchainbased HIE. Software components and implementation of MEXchange on the Ethereum private network are discussed. We evaluate MEXchange quantitatively by measuring the transaction latency and throughput of exchanging. Also, we evaluate MEXchange qualitatively using the requirements of the Office of National Coordinator for Health Information Technology (ONC). Moreover, we proceed threat modeling based on STRIDE. Finally, we compare MEXchange with Ancile, FHIRChain, Integrating the Healthcare Enterprise Cross-Enterprise Document Sharing (IHE XDS), and MedRec. The MEXchange lowers barriers to the application of blockchain-based HIE systems by mitigating privacy and security issues among healthcare stakeholders.

Healthchain: A Blockchain-Based Privacy Preserving Scheme for Large-Scale Health Data

With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users’ health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.

Parking Management: A Blockchain-Based Privacy-Preserving System

In a smart city, smart parking systems generally strive to facilitate the renting of available private parking spaces to vehicle users (just like timesharing). Existing technologies can authenticate and manage parking spaces and vehicles, but they do not address the need to preserve users' privacy. This article presents the framework for a blockchainbased parking-management system designed to preserve the privacy of its users, without relying on a reliable third-party entity. The proposed system integrates BlockChainOpenSource (BCOS) and smart contract technology to share parking spaces.