In this paper, the first data trading certification blockchain solution based on consortium blockchain and smart contracts is proposed to solve the certification data security problem in data trading, so as to realize the auditability, accountability and integrity of data trading. By the proof-of-authority algorithm, a cheap-and-quick consortium blockchain is built. By the consortium blockchain, smart contracts can be deployed safely and conveniently. By the Solidity language, a concise-and-effective certification data smart contract and data trading smart contract are constructed to ensure the certification data security. By deploying the certification data smart contract and the data trading smart contract on the consortium blockchain, the security, transparency and supervisability of certification data is carried out. By utilizing the consortium blockchain and smart contracts, the data trading certification model is established to ensure the certification data security in data trading. By the experiments, the consortium blockchain is successfully established, and the certification data smart contract and the data trading smart contract are successfully deployed, so that the certification data security is effectively guaranteed. Finally, by the Github, the source code of the certification data smart contract and the data trading smart contract is uploaded.
The development of the Internet of Things (IoT) technology has achieved remarkable results in recent years. A large number of sensors and machinery are connected, and combined with the Internet, to achieve intelligent management and operation. However, the traditional centralized IoT data management solution inevitably encounters challenges of data trust, security, sustainability, and user privacy is difficult to guarantee. This paper takes the intelligent transportation sensor network as an example and proposes a blockchain-based Internet of Vehicles (IoV) data secure sharing scheme (called IoVChain), which implements automatic registration, rapid authentication, and reliable sharing method of IoV data through smart contract. The smart contract performs homomorphic encryption and zero-knowledge proof processing on the sensitive part of the data, and this part exists in the form of ciphertext on the blockchain. We adopt PBFT consensus mechanism to ensure the consistency of the entire network ledger, and all the IoV data processing and usage procedure is stored in the Merkle-tree-based block, which cannot be tampered with. Compared with the centralized IoV data management solution, the IoVChain scheme avoids the risk of single point of failure, and keeps the IoV data credible, available, and tamper-resistant under the premise of privacy and security, so that the data can be traced when needed. We have implemented the IoVChain scheme based on the consortium blockchain for trusted and secure sharing of IoV data. Finally, analysis and evaluation show that the proposed IoVChain scheme is feasible, safe, and extensible for the secure sharing of IoV data.
For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.
Academic credentials are precious assets as they form an evidence for one’s identity and eligibility. Fraud inissuance and verification of academic certificates have been a long-standing issue in academic community. Due to lack of antiforgery mechanisms there has been substantial increase in fraudulent certificates. The need of the hour is to have a transparent and reliable model for issuing and verifying academic certificates to eliminate fraud in the process. Decentralized, Auditable and Tamper-proof properties of Blockchain makes it possibly the best choice for issuing and verifying academic certificates. In this paper we propose a model, where regulatory body authorizes higher education Institutes (universities and colleges) for issuing academic certificates to students in a decentralized way. Anyone in the world can verify the authenticity of the certificate by triggering appropriate smart contract functions, thus eliminating any possibility of fraud in the process. In addition we used multi signature scheme where certificates are required to be signed by designated authority from Higher Education Institutes, thus allowing for multi-level checks on certificate contents before being successfully deployed on Blockchain. We have also provide Proof of Concept in Ethereum Blockchain and evaluated its performance in terms of cost, security and scalability.
In blockchain-based anonymous cryptocurrencies, due to their tamper-resistance and transparency characteristics, transaction data are initially required to be anonymous, with the help of various cryptographic techniques, e.g., commitment schemes and zero-knowledge proofs. Also, cryptocurrencies are different from existing anonymous messaging protocols regarding the software architecture and the underlying security model. Due to these differences, the sense of anonymity must be specifically defined for anonymous cryptocurrencies, and the anonymity in each anonymous cryptocurrency must be analyzed and evaluated based on the specific architecture model. In this paper, we first propose a specific architecture model with three software layers to anonymous cryptocurrencies. Next, we introduce definitions of fundamental privacy properties (Pfitzmann's anonymity, unlinkability, and pseudonymity) and comprehensively analyze each privacy property for each architecture layer of anonymous cryptocurrencies to establish a privacy evaluation policy for anonymous cryptocurrencies. Finally, we fairly compare the privacy of current leading anonymous cryptocurrencies (e.g., Zerocash, CryptoNote, and Mimblewimble) using the privacy evaluation policy.
Md. Anwar Hussen Wadud, T. M. Amir-Ul-Haque Bhuiyan, Md. Ashraf Uddin, Md. Motiur Rahman
Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database.
With the prosperity of wireless and mobile communications, the allocation of wireless resources, e.g., spectrum channels, femtocell access permissions, and resource blocks of D2D connections, has become a matter of great concern, which leads to the emergence and popularity of electronic auction. However, the lack of privacy protection and fairness guarantee in the existing market has posed great obstacles to user participation in electronic auctions. Without privacy protection, users will be worried about the unauthorized exposure of their commercial secrets, which may hurt their payoffs in the long run. Without fairness guarantee, selfish/malicious users may prematurely abort the auction to avoid unsatisfactory payment, thus causing loss to honest users and wasting system resources. Furthermore, the lack of fairness guarantee will incur an exchange dilemma, where mutually distrusting parties may refuse to initiate the goods/money exchange, hindering the market-clearing of auctions. Although extensive efforts have been made to develop privacy-preserving auction mechanisms for wireless markets, the fairness issue, to the best of our knowledge, has never been taken into account in the context of auctions. Moreover, a truly practical framework that can be applied to most mainstream auction formats is also missing in the literature. In this article, we present the first general framework, named SAFE, for representative auctions in (but not limited to) wireless markets. SAFE achieves a wide range of security goals with much higher efficiency than existing solutions. Furthermore, SAFE ensures auction fairness in a trust-free manner by developing a full set of carefully-designed modular protocols that can be easily adapted to any auction format. We implement the SAFE framework over a simulated Ethereum network. Extensive experimental results confirm that SAFE can indeed achieve high economic efficiency in terms of social welfare and participation satisfaction at very low computation and communication overheads.
For large-scale data transmission of the Industrial Internet of Things (IIoT), aggregate signature is an effective approach. It can compress the signatures of different senders to save bandwidth. In order to maintain the autonomous management of IIoT, massive sensing data are sent to the data center for intelligent analysis. The reliability of data is an important guarantee of the autonomous management of IIoT. Tracing abnormal senders is a challenge when hiding their real identity. Therefore, we design the first permissioned blockchain-based anonymous and traceable aggregate signature (PBATAS) scheme for IIoT. Smart contracts are used to authenticate anonymous sources and share cryptographic materials among entities, providing reliable regulatory support for IIoT. The regulator can quickly trace the abnormal data sources recorded on the blockchain, which is practical for the anonymous IIoT environment. Through the formal security proof of conditional anonymity, unforgeability, traceability, and resistance to coalition attacks, the proposed PBATAS is provably secure. Performance analysis demonstrates that PBATAS is effective.
As more and more organizations deploy their sensing devices in the industrial Internet of Things network, it becomes increasingly important for the organizations to share data with others, so that the value of the data can be fully explored. However, individuals' privacy may be compromised because of data sharing. In this article, we study the differentially private data publishing problem, which aims at balancing between privacy and data utility. Specifically, two blockchain-based data publishing protocols are proposed. For histogram publishing, we propose a protocol where the Laplace noise added in the query result is verified by the blockchain. For anonymized data publishing, we propose a protocol, which can prevent the publisher and the recipient from lying about the utility of the published data. With the blockchain acting as a reliable intermediary between the publisher and the recipient, the proposed protocols can help to realize fair and auditable data sharing.
With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
Chenyue Zhang, Wenjia Li, Yuansheng Luo, Yupeng Hu
Currently, connected vehicles have gradually stepped into our daily lives, and they generally rely on vehicular networks to generate and exchange traffic-related messages to improve the overall travel safety and efficiency. However, due to the open nature of vehicular networks, these traffic-related messages could be erroneous, which may be caused by various reasons, ranging from an onboard device (OBD) sensor malfunctioning and reporting incorrect reading to the message being tampered by a malicious vehicle. To address these rapidly increasing security challenges, we have proposed an AI-enabled trust management system (AIT) in this article, which is an AI-enabled trust management system for vehicular networks using the blockchain technique. In the AIT system, each vehicle first senses, generates, and exchanges messages with other vehicles. These messages then get validated by the neighboring vehicles. As vehicles receive and validate messages from other nearby vehicles, they will establish and manage the trust of those nearby vehicles, which is enabled by utilizing the deep learning algorithm. Once a vehicle identifies untrustworthy vehicles, it reports them to the nearby roadside unit (RSU), and the RSU will validate the authenticity of the report as well as the identity of the vehicle by using the emerging blockchain technique. The security credentials of untrustworthy vehicles will then be revoked by the RSU. We have conducted an extensive experimental study to evaluate the AIT system. Simulation results clearly indicate that AIT performs better than existing approaches and can manage the trust of vehicles and detect malicious ones in an accurate and efficient manner.
Francisco José de Haro-Olmo, Ángel Jesús Varela‐Vaca, José Antonio Álvarez Bermejo
The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.
Crowdsourcing aims to deliver services and content by aggregating contributions from a large user population. For mobile networks and IoT systems, crowdsourcing is used to gather and process sensor data from mobile devices (crowdsensing), in order to deliver real-time, context-aware services and possibly support user collaboration in extended geographic areas. In applications like geonsensitive navigation, location-based activity sharing and recommendations, the challenge of adequate service quality and user experience may be at stake, as the services are provided securely to an ever-growing user population. This happens due to the inherent trade-off between security and real-time performance that ultimately sets in doubt any scalability prospect beyond a certain user-interaction load. This work introduces a publish-subscribe architecture for mobile crowdsensing systems, which can be transparently scaled up to higher usage load, while retaining adequate performance and security by load balancing into multiple MQTT brokers. The security support combines a lightweight TLS implementation with an integrated mechanism for two-level access control: user-device interactions and message topics. We provide proof-of-concept measurements that show how our solution scales to increasing interaction loads through load-balancing the processing cost that includes the overhead of the security mechanisms applied. The system architecture was implemented in a vehicular crowdsensing navigation network that allows to exchange navigation information at real-time, for improved routing of vehicles to their destination.
In the era of big data, the ownership of digital asset has become an important issue that people concern about. Traditional means of data right confirmation adopt the mode of submitting the ownership evidence and authority reviewing, but there are uncontrollable factors such as potential tampering. In order to solve this problem, a data right confirmation mechanism based on blockchain and locality sensitive hashing is proposed. First, blockchain technology is used to handle the matter of trust in data right confirmation, and a user identity management scheme based on Hyperledger Fabric is proposed to ensure the credibility of the identity of the confirmation entity. Secondly, a data fingerprint extraction scheme based on locality sensitive hashing is proposed, and it is used to maintain the consistency of the fingerprints stored on the blockchain and the data disseminated off the blockchain. Finally, the chaincode deployed on Hyperledger is developed to support the submitting, verification and notarization of confirmation transactions. And the security SDK components are developed to interact with the chaincode. The result of experiment shows that this mechanism can effectively discover the infringement of data resources with the appropriate parameters.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In order to realize the sharing of data by multiple users on the blockchain, this paper proposes an attribute-based searchable encryption with verifiable ciphertext scheme via blockchain. The scheme uses the public key algorithm to encrypt the keyword, the attribute-based encryption algorithm to encrypt the symmetric key, and the symmetric key to encrypt the file. The keyword index is stored on the blockchain, and the ciphertext of the symmetric key and file are stored on the cloud server. The scheme uses searchable encryption technology to achieve secure search on the blockchain, uses the immutability of the blockchain to ensure the security of the keyword ciphertext, uses verify algorithm guarantees the integrity of the data on the cloud. When the user's attributes need to be changed or the ciphertext access structure is changed, the scheme uses proxy re-encryption technology to implement the user's attribute revocation, and the authority center is responsible for the whole attribute revocation process. The security proof shows that the scheme can achieve ciphertext security, keyword security and anti-collusion. In addition, the numerical results show that the proposed scheme is effective.
In traditional centralized application construction and data storage modes, the application servers and the users are not in equal position in user information management. The application server can store and abuse the user's information under the circumstances that the users are not aware, and the user cannot revoke the authorization from the application server. Therefore, this paper proposes an identity management system combined with blockchain technology, which can return the management right of user information to users. This system adopts a three-layer architecture composed of blockchain layer, virtual chain layer and storage layer, and encapsulates all functions in each layer. It has good flexibility, and is easy to add new technologies, with good extensibility. The blockchain records the user's information state to realize the consensus and integrity of the user's data. The virtual chain layer is responsible for the main logical functions and encapsulates the user's request into a blockchain transaction, maintaining a good portability. The storage layer is responsible for the actual file storage and is responsible for file routing, backup, and query. Comprehensively speaking, based on the above three layers' architecture, the system implements the user's privacy granularity control, the user file's external storage, and combines the zero-knowledge proof to update the user's private key. In view of the application environment of this system, the access time delay and efficiency of the P2P storage system are simulated. It solves the problems of centralized application construction and data storage mode under which users cannot fully control personal information and privacy protection.
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of distributed and federated learning. Our first contribution is a novel distributed differentially private protocol which naturally scales with the number of parties. The key idea underlying our protocol is to exchange correlated Gaussian noise along the edges of a network graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of our protocol and the impact of the graph topology, showing that we can match the accuracy of the trusted curator model even when each party communicates with only a logarithmic number of other parties chosen at random. This is in contrast with protocols in the local model of privacy (with lower accuracy) or based on secure aggregation (where all pairs of users need to exchange messages). Our second contribution is to enable users to prove the correctness of their computations without compromising the efficiency and privacy guarantees of the protocol. Our construction relies on standard cryptographic primitives like commitment schemes and zero knowledge proofs.
Muhammad Shayan, Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh
Federated Learning is the current state-of-the-art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to both protect the privacy of an individual client's update and maintain the performance of the global model at scale when 30 percent adversaries are present in the system.
Kavinga Yapa Abeywardena, Buddhima Attanayaka, Kabilashan Periyasamy, S. A. C. M. Gunarathna · 6 authors
In the data technology revolution, electronic medical records are a standard way to store patients' information in hospitals. Although some hospital systems using server-based patient detail management systems, they need a large amount of storage to store all the patients' medical reports, therefore affecting the scalability. At the same time, they are facing several difficulties, such as interoperability concerns, security and privacy issues, cyber-attacks to the centralized storage and maintaining adhering to medical policies. Proposed Flexi Medi is a private blockchain based patient detail management system which is expected to address the above problems. Solution proposes a distributed secure ledger to permits efficient system access and systems retrieval, which is secure and immutable. The improved consensus mechanism achieves the consensus of the data without large energy utilization and network congestion. Moreover, Flexi Medi achieves high data security principles based on a combination of hybrid access control mechanism, public key cryptography, and a secure live health condition monitoring mechanism. The proposed solution results in successfully deployed smart contracts according to the roles of the system, real time patient health monitoring with more scalable and access controlled system. The overall objective of this solution is to bring the entire medical industry into a common platform using a decentralized approach to store, share medical details while eliminating the need to maintain printed medical records.
Rakib Ul Haque, A S M Touhidul Hasan, Qingshan Jiang, Qiang Qu
Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.