Privacy-preserving Blockchain Research Articles

Energy-Efficient and Privacy-Preserving Blockchain Based Federated Learning for Smart Healthcare System

Energy-Efficient and Privacy-Preserving Blockchain Based Federated Learning for Smart Healthcare System

Differential privacy model for blockchain based smart home architecture

Secure and private communications using the Internet of Things (IoT) pose several challenges for smart home systems. In particular, data collected from IoT devices comprise sensitive personal information such as biomedical data, financial data, and location and activity data. Recent research looks into the use of blockchain in smart home systems, protecting the privacy of the data in use. Such solutions need to address the issue of privacy using a formal and mathematical model for data privacy due to the vulnerability associated with privacy-preserving blockchain networks. In the present paper, our approach aims to provide a privacy-preserving data aggregation mechanism in the context of Smart Homes that agree to contribute their data to a cloud server using machine learning to improve services for home users. We propose the use of differential privacy, a powerful concept in privacy preserving schemes to provide formal assurances about how much information is leaked using a privacy budget. The main purpose of using such a privacy-preserving scheme is to limit what can be inferred about individual training data from the model. Our techniques use a R′enyi differential privacy (RDP) machine learning scheme and are based on a variant of the stochastic gradient descent function. The performance of our proposed framework is evaluated using three public datasets: UNSW-NB15, NSL-KDD, and ToN-IoT datasets. Our findings show that differential private models can provide privacy protection against attackers by sacrificing a substantial amount of model utility. Therefore, we propose an empirical value of ϵ, that can optimally balances utility and privacy for the current smart home scenario datasets.

Security-Aware and Privacy-Preserving Blockchain Chameleon Hash Functions for Education System

The most crucial properties of decentralized, immutable blockchain technologies are being transparent, tamper-proof, and have total traceability. With an increase in overseas students globally, the problem of diploma forgery, and the sale of forged credentials, the management and dissemination of student educational information continue to encounter several problems. Privacy violation issues like security, privacy, trustworthiness, consistency challenges, and traceability issues are considered when managing student academic records in educational sectors. The proposed work is a novel decentralized Chameleon Hash Function and it is applied to overcome these privacy violation issues. Security-aware and privacy-preserving blockchain Chameleon hash functions for Education System are suggested because every redaction needs to be approved by numerous blockchain nodes. A Proof of Continuous Work (PoCW) consensus algorithm entirely based on Blockchain is proposed for data storage and sharing, which minimizes processing power wastage to enhance the accessibility and transparency of the procedure for students receiving educational degree certificates. By consistently giving proof of storage, miners can gain an edge in the mining procedure. Without any outside help, Blockchain has created a reliable blockchain-based storage system that does not depend on a third party. The simulation and theoretical study's findings demonstrate that the proposed scheme has enhanced security, trustworthiness, and traceability.

An efficient privacy-preserving blockchain storage method for internet of things environment.

Blockchain is a key technology to realize decentralized trust management. In recent studies, sharding-based blockchain models are proposed and applied to the resource-constrained Internet of Things (IoT) scenario, and machine learning-based models are presented to improve the query efficiency of the sharding-based blockchains by classifying hot data and storing them locally. However, in some scenarios, these presented blockchain models cannot be deployed because the block features used as input in the learning method are privacy. In this paper, we propose an efficient privacy-preserving blockchain storage method for the IoT environment. The new method classifies hot blocks based on the federated extreme learning machine method and saves the hot blocks through one of the sharded blockchain models called ElasticChain. The features of hot blocks will not be read by other nodes in this method, and user privacy is effectively protected. Meanwhile, hot blocks are saved locally, and data query speed is improved. Furthermore, in order to comprehensively evaluate a hot block, five features of hot blocks are defined, including objective feature, historical popularity, potential popularity, storage requirements and training value. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the proposed blockchain storage model.

Secure authentication and privacy-preserving blockchain for industrial internet of things

Blockchain (BC) technology has overtaken Industrial Internet of Things (IIoT) platforms. It is necessary to explore efficient implementation. Fault tolerance, decentralised control, authentication, cryptographic security, immutability, data integrity, and BC smart contracts are recommended IIoT features. If entities are authenticated and trusted, the internet can be used for industrial activities. Despite several methods, communication is insecure due to scalability, dependability, latency, insufficient transmission security, and uneven data loads. The paper created safe User authentication and optimal BC node selection using AFHENN (Fully Homomorphic encryption neural network) for IIoT to solve the problem. Mutual authentication, secrecy, and integrity protect user data. A registration process secures new User authentication. To protect registered data, it uses cryptographic methods like Transient key congruential generator based Elliptic curve cryptography (TKCG-ECC) and Dual keyed Cipolla's Extended Euclidean Algorithm based lattice cryptosystem (DKCEED-LC). To access BCN, the gateway verifies registered users utilising keyed-based Zero Knowledge of Proof (k-ZKP) and Approximation Fully Homomorphic encryption neural network-based Blockchain. Finally, Approximation Fully Homomorphic encryption neural network-based Blockchain networking authenticates data (AFHENN-BCN). The BCN avoids legal selection of miner nodes and harmful activities. Compared to top techniques, the proposed work achieves improved throughput and PDR (Packet Delivery Ratio) values with minimal computing time and strong security.

Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO)

The fierce competition in international markets and the rapid advancements in information technology result in shorter lead times, lower transportation capacity, and higher demand. The supply chain network is one of the most crucial areas of concentration in the majority of business circumstances. Blockchain technology is a promising option for safe information exchange in the supply chain network. Although preserving security at every level of the blockchain is somewhat important, cryptographic methodologies are frequently used in the existing works. The novel perceptive craving game search (PCGS) optimization algorithm is used to optimally generate the key for data sanitization, which assures the privacy of logistics data. Here, the original logistics data obtained from the manufacturer is sanitized with an optimal key generated by using the PCGS optimization algorithm, avoiding the risk of unauthorized access and data swarm that causes the system to lag. Moreover, the sanitized data obtained from the manufacturer is transmitted to the allowed parties via different sub-chains. The same generated key is used on the receiving customer side for reconstructing the original information from the sanitized data. The performance and results of the proposed blockchain-based privacy preservation model are validated using various parameters.

A Privacy-Preserving Blockchain Platform for a Data Marketplace

Recent data leak scandals, together with the under-utilization of collected data (estimated that around 90% of data never leaves a device’s local storage), limits the applicability and potential of novel data driven applications. Thus, novel ways to treat data, in which users are guaranteed control, usability, and privacy over their generated data are needed. In this paper we propose a novel privacy-preserving blockchain framework for a data sharing marketplace. The proposed framework allows users (such as sensors and devices) that generate data to store it in external servers, while the blockchain is utilized to record buy and sell transactions between parties, as well as perform access control by generating access sequences whenever trades are performed. A novel perspective over data ownership is presented, in which whoever generates the data has completed ownership and control over it and the blockchain transactions are only utilized to guarantee temporary access to it. The proposed blockchain framework also supports different types of data and provides, via the distributed and openness of the framework, quality, timeliness and similarity control over the data stored in the marketplace. In this context, different types of applications that can benefit from this framework are presented and open problems are discussed.

Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems

An Optimal Algorithm for Secure Transactions in Bitcoin Based on Blockchain

Technological advancement has made a significant contribution to the change of the economy and the advancement of humanity. Because it is changing how economic transactions are carried out, the blockchain is one of the technical developments that has a lot of promise for this progress. The public record of the Bitcoin blockchain provides dispersed users with evidence of transaction ownership by publishing all transaction data from block reward transactions to unspent transaction outputs. Attacks on the public ledger, on the other hand, are a result of the fact that all transaction information are exposed. De-anonymization attacks allow users to link transaction entities and acquire user privacy through specified transaction amounts. As a result, in light of the Bitcoin blockchain system’s privacy issues, this scheme combines the concept of coin mixing with encrypted transaction technology to create a truly anonymous blockchain system that preserves the payer identity and transaction amount privacy. The one-way aggregated signature technique of Boneh, Gentry, and Lynn systematically embeds the notion of mixing into the whole block. The homomorphic encryption approach of Boneh, Goh, and Nissim allows miners to check the legality of encrypted transactions. Miners will validate transactions, conceal transactions, and package transactions as entities in the scheme. Finally, this technique was chosen after a comparison of several privacy-preserving blockchain schemes. It not only ensures complete anonymity, but also keeps transaction storage overhead to a minimum.

HyperMaze: Towards Privacy-Preserving and Scalable Permissioned Blockchain

Blockchain systems face two emergent problems, namely scalability and privacy, each of which has been addressed independently. However, how to achieve privacy and scalability simultaneously remains a challenging problem for blockchains. In this article, we propose a privacy-preserving and scalable permissioned blockchain system called HyperMaze employing the zero knowledge proof technique and a hierarchical system architecture. It gains scalability by adopting a hierarchy of multiple blockchains that processes transactions in parallel. We design an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ID-based dual-balance account model</i> where an identity-based account is associated with two types of balances–a plaintext balance and a private (zero-knowledge) balance. Furthermore, we design a two-phase cross-chain transaction mechanism (2PXT) to achieve transaction privacy for both intra-chain and cross-chain transactions. We rigorously formulate a security model for HyperMaze under the universal composability framework, and then provide a simulation-based security proof. A prototype of HyperMaze is implemented and a series of experiments are conducted over up to 2,600 nodes to evaluate its performance. The experimental results show that a 4-level, (7,10)-threshold, 6-ary HyperMaze system can reach 19,440 TPS and the transaction confirmation latency is only 9.5 seconds. To our best knowledge, HyperMaze is the first high-throughput privacy-preserving blockchain whose throughput is over 19,000 TPS.

A Trust-Centric Privacy-Preserving Blockchain for Dynamic Spectrum Management in IoT Networks

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

TPPSUPPLY : A traceable and privacy-preserving blockchain system architecture for the supply chain

Traceability and auditability are key structures in supply chain management and construction. However, trust is the most important aspect of customers in these systems. Also, relying on third parties to trade in centralized systems is indispensable. Although current exist frameworks for these solutions in the supply chain, these have work poor traceability and lack of real-time information, and especially lack of privacy. In this article, we propose a framework for supply chain traceability that preserves privacy from third parties by using smart contracts. In the proposed framework, digital signature and verification are provided by using existing cryptographic techniques in off-chain and on-chain smart contract integration, and it is demonstrated the applicability of architecture by testing. The architecture also provides both anonymity and traceability depending on the user’s request. Finally, thanks to traceability and auditability in the proposed system, customers and other parties can view with a single product ID and also verify with digital signature the claims of the actors in the system.

Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources. Despite the advantages of smart cities, security remains a huge challenge to be overcome. Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario. Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization. In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model. The proposed PPSF-BODL model includes the collection of primary data using sensing tools. Besides, z-score normalization is also utilized to transform the actual data into useful format. Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions. CSO is employed for optimal hyperparameter tuning of ABiLSTM model. At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server. This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods. A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

HOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design

In the context of blockchain technology, “off-chain” refers to computation or data that is structurally external to the blockchain network. Off-Chain Blockchain Systems (OCBS) enable this information processing and management through distributed software architecture where the blockchain network interacts with off-chain resources. Hence, OCBS are a critical data governance component in the design of enterprise blockchain solutions, resulting in extensive research and development exploring the interplay between on-chain and off-chain storage and computation and efforts to evaluate their performance relative to other information management systems. Key features of OCBS’ are their ability to improve scalability, reduce data storage requirements, and enhance data privacy, all extremely critical issues to enable broader blockchain adoption. These OCBS features map well to the needs of the healthcare industry, particularly due to the need to manage various types of medical, consumer, and other health-related data. However, different types of health data are also subject to stringent regulatory, security and legal requirements, a key factor limiting blockchain adoption in the sector. In response, there is a critical need to better align OCBS design features to different types of healthcare data management and their respective governance and privacy regimes. This article first reviews the characteristics of different constructs of OCBS. It then proposes a modular hybrid privacy-preserving framework leveraging off-chain and on-chain blockchain system design applied to three different reference models that illustrate how blockchain can enhance healthcare information management. Through this privacy-preserving framework we hope to liberate healthcare data by enabling sharing, sovereignty and enhanced trust.

Edge-Based Blockchain Architecture for Event-Driven IoT using Hierarchical Identity Based Encryption

IoT (Internet of Things) sensors usually generate a large amount of data that is shared with a centralized cloud to provide various services. Traditional IoT architecture is heavily centralized, where data stored in a cloud environment is prone to several kinds of threats. As such, both data generated by sensors as well as IoT network shall be protected. Blockchain is an emerging technology that can be used in a wide spectrum of applications for better protection. However, the traditional blockchain protocol for Bitcoin cannot be used for IoT data protection because Bitcoin is a payment system, and the IoT ecosystem has a different architecture and requirements. Hence, more suitable blockchain architecture and encryption schemes for IoT are needed. In this paper, we propose a privacy-preserving blockchain architecture for IoT using Hierarchical Identity Based Encryption (HIBE). The proposed architecture fits well with event-driven IoT devices and uses the edge and the cloudlet computing paradigm as well as HIBE to preserve the privacy. Furthermore, the efficiency of the proposed architecture is demonstrated using a real-life case, namely, traffic speed radars. The security of the proposed architecture is analyzed and is verified through theoretical analysis that considers the existence of a malicious adversary. The performance of the proposed architecture is evaluated by conducting extensive experiments using the Contiki OS. The experimental results provide the system and network performance of the proposed architecture.

A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture

Blockchain and smart contracts have seen significant application over the last decade, revolutionising many industries, including cryptocurrency, finance and banking, and supply chain management. In many cases, however, the transparency provided potentially comes at the cost of privacy. Blockchain does have potential uses to increase privacy-preservation. This paper outlines the current state of privacy preservation utilising Blockchain and Smart Contracts, as applied to a number of fields and problem domains. It provides a background of blockchain, outlines the challenges in blockchain as they relate to privacy, and then classifies into areas in which this paradigm can be applied to increase or protect privacy. These areas are cryptocurrency, data management and storage, e-voting, the Internet of Things, and smart agriculture. This work then proposes PPSAF, a new privacy-preserving framework designed explicitly for the issues that are present in smart agriculture. Finally, this work outlines future directions of research in areas combining future technologies, privacy-preservation and blockchain.

Achieving efficient and Privacy-preserving energy trading based on blockchain and ABE in smart grid

With the advent of the Industry 4.0 era, the development of smart cities based on the Internet of Things (IoT) has reached a new level. As a key component of the Internet of Things (IoT), the security of wireless sensor networks (WSN) has received widespread attention. Among them, Energy Internet, as an important part to support the construction of smart cities, its security and reliability research is becoming more and more important. In the Energy Internet, distributed energy transaction model is a promising approach to replace the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy disclosure. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transactions called PP-BCETS (Privacy-preserving Blockchain Energy Trading Scheme). It can achieve fine-grained access control through transaction arbitration in the ciphertext form. This design can maximize the protection of privacy information, and considerably improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCETS, which can greatly increase the operation efficiency. The security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

Can You Trust the Blockchain? The (Limited) Power of Peer-to-Peer Networks for Information Provision

We investigate the potentials and limits of privacy-preserving blockchain technology for information provision. In our model, heterogeneous firms can adopt a privacy-preserving blockchain or rely on traditional institutions. The blockchain leverages its peer-to-peer architecture to disseminate an aggregate signal about each firm's valuation. The firm-specific information provision depends on two factors: (i) the blockchain's fit for analyzing a given firm's data, and (ii) its reach into the economy. The technology can improve information provision in two ways. The adoption decision itself may serve as a credible signal of a firm's valuation, and the blockchain may generate more information than traditional institutions when its reach is sufficiently high. However, we characterize an equilibrium in which high-value and low-value firms are present both inside and outside the blockchain, which limits both channels' ability to generate information. The overall information provision can even fall below the benchmark case in which blockchain technology is not available.