Rongyu Xiao, Guozi Sun, Jiale Yang, Yao Wang · 5 authors
Public chains represented by Bitcoin and Ethereum do not require users to use their real names, and transaction data are open to the whole network. Analysed based on this, researchers have achieved the deanonymization of blockchain transactions to a certain extent. Based on the existing blockchain transaction privacy protection scheme, the true link relationship between the transaction sender and receiver is hidden, which brings difficulties to regulation. In this paper, we propose a cryptocurrency mixing service RBSmix, which allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. RBSmix, through blind signature to prevent attackers from linking input and output addresses, by the threshold secret sharing algorithm, encryption technology, and a regulation team, combined with the idea of voting, tracks the source of funds for illegal addresses. Experiments show that the scheme scales to large numbers of users and can provide users with better privacy protection.
Blockchain technology has certainly revolutionized the world. A decentralized approach devoid of a centralized server/third party access provides complete anonymity, security, and integrity. But still, there is some vulnerability in Blockchain, one of them we discussed in this paper is Front-Running. Front running is mostly exercised in the stock market sector where the traders try to front-run to gain more profit. Now as blockchain technology is flourishing at an immense rate, new forms of front running techniques have been encountered. Thus creating transaction vulnerabilities. In this paper, we explained the front-running issues on MCS-Dapps deployed on Ethereum Blockchain. Furthermore, we will study its working on the system and how it affects clients and miners on their gas prices. Then we implemented a commit-reveal strategy to increase the transaction confidentiality to counter the issue of front running.
Krishan Kumar, Jenifer Mahilraj, D. Swathi, R. Rajavarman · 8 authors
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%.
Data management is the collection, processing, storing, and sharing of data. In today's dispensation, data sharing and collaborative data processing is a necessity for multi-partner organizations as it can lead to the discovery of new insight. Shared data is generally for the purpose of marketing, advertising, and other institutional decision-making reasons. A challenge however is that the collected data (mostly about individuals known as data subjects or producers) is disseminated among organizations without meaningful consent from the data subjects. Hence, data subjects are not aware of what is happening to their data regarding use and misuse. Furthermore, data subjects can hardly determine which third-party institutions have access to their data. In this paper, we opined that data should be managed in a manner that can persuade the trust of data subjects. To achieve this, data subjects should have the rights to be informed about details of their data. Thus, this paper proposes a cloud-based data management and sharing platform to enable data subjects to control who can access their data and consent to its collection and usage based on smart contracts. The proposed system leverages blockchain to enforce accountability, provenance, and auditability of all events. We implemented a dynamic consent management prototype on top of Ethereum blockchain to demonstrate the feasibility of the proposed work.
Xihua Zhang, S. B. Goyal, Miretab Tesfayohanis, Chaman Verma
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.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
The electronic know your customer (e-KYC) is a system for the banking or identity provider to establish a customer identity data verification process between relying parties. Due to the efficient resource consumption and the high degree of accessibility and availability of cloud computing, most banks implement their e-KYC system on the cloud. Essentially, the security and privacy of e-KYC related documents stored in the cloud becomes the crucial issue. Existing e-KYC platforms generally rely on strong authentication and apply traditional encryption to support their security and privacy requirement. In this model, the KYC system owner encrypts the file with their host’s key and uploads it to the cloud. This method induces encryption dependency and communication and key management overheads. In this paper, we introduce a novel blockchain-based e-KYC scheme called e-KYC TrustBlock based on the public key encryption method binding with the client consent enforcement to deliver trust, security and privacy compliance. In addition, we introduce attribute-based encryption to enable the privacy preserving and fine-grained access of sensitive transactions stored in the blockchain. Finally, we conduct experiments to show that our system is efficient and scalable in practice.
Ibrahim Abunadi, Maha M. Althobaiti, Fahd N. Al‐Wesabi, Anwer Mustafa Hilal · 8 authors
The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.
In recent years, cloud-based medical record sharing has greatly improved the process of researching the disease and patient diagnosis. However, since cloud systems are centralized, there is serious concern about data security and privacy. Blockchain technology is viewed as a promising method of dealing with privacy issues and data security because of its exclusive features of distributed ledgers, secrecy, verifiability, and enhanced security. The literature review has shown significant works on integrating blockchain technology with cloud system for managing and sharing healthcare data. It has been analyzed that previous works are primarily dependent on the centralized data storage approach, which raises privacy concerns. The previous works also do not emphasize handling big medical data and lack the reliability of the end-to-end security features system. This paper has presented an authorization framework for ensuring data security and privacy preservation using blockchain technology with IPFS as decentralized file storage and sharing system. The proposed study devises a proof of replication algorithm using smart contracts to provide a better access control mechanism. The implementation of the proposed framework is based on the symmetric encryption and Ethereum blockchain platform. The study outcome illustrates the efficiency and availability of the proposed scheme compared to the typical cloud-based blockchain method.
Identity Management Systems (IdMS) have seemingly evolved in recent years, both in terms of modelling approach and in terms of used technology. The early centralized, later federated and user-centric Identity Management (IdM) was finally replaced by Self-Sovereign Identity (SSI). Solutions based on Distributed Ledger Technology (DLT) appeared, with prominent examples of uPort, Sovrin or ShoCard. In effect, users got more freedom in creation and management of their identities. IdM systems became more distributed, too. However, in the area of interoperability, dynamic and ad-hoc identity management there has been almost no significant progress. Quest for the best IdM system which will be used by all entities and organizations is deemed to fail. The environment of IdM systems is, and in the near future will still be, heterogenous. Therefore a person will have to manage her or his identities in multiple IdM systems. In this article authors argument that future-proof IdM systems should be able to interoperate with each other dynamically, i.e. be able to discover existence of different identities of a person across multiple IdM systems, dynamically build trust relations and be able to translate identity assertions and claims across various IdM domains. Finally, authors introduce identity relationship model and corresponding identity discovery algorithm, propose IdMS-agnostic identity discovery service design and its implementation with use of Ethereum and Smart Contracts.