Collins Sey, Hang Lei, Weizhong Qian, Xiaoyu Li · 8 authors
The rapid advancement of the Internet of Vehicles (IoV) has led to a massive growth in data received from IoV networks. The cloud storage has been a timely service that provides a vast range of data storage for IoV networks. However, existing data storage and access models used to manage and protect data in IoV networks have proven to be insufficient. They are centralized and usually accompanied by a lack of trust, transparency, security, immutability, and provenance. In this paper, we propose VBlock, a blockchain-based system that addresses the issues of illegal modification of outsourced vehicular data for smart city management and improvement. We introduce a novel collusion-resistant model for outsourcing data to cloud storage that ensures the network remains tamper-proof, has good data provenance and auditing, and solves the centralized problems prone to the single point of failure. We introduced a key revocation mechanism to secure the network from malicious nodes. We formally define the system model of VBlock in the setting of a consortium blockchain. Our simulation results and security analysis show that the proposed model provides a strong security guarantee with high efficiency and is practicable in the IoV environment.
Daniël Reijsbergen, Aung Htein Maw, Zheng Yang, Tien Tuan Anh Dinh · 5 authors
Users today expect more security from services that handle their data. In addition to traditional data privacy and integrity requirements, they expect transparency, i.e., that the service's processing of the data is verifiable by users and trusted auditors. Our goal is to build a multi-user system that provides data privacy, integrity, and transparency for a large number of operations, while achieving practical performance. To this end, we first identify the limitations of existing approaches that use authenticated data structures. We find that they fall into two categories: 1) those that hide each user's data from other users, but have a limited range of verifiable operations (e.g., CONIKS, Merkle2, and Proofs of Liabilities), and 2) those that support a wide range of verifiable operations, but make all data publicly visible (e.g., IntegriDB and FalconDB). We then present TAP to address the above limitations. The key component of TAP is a novel tree data structure that supports efficient result verification, and relies on independent audits that use zero-knowledge range proofs to show that the tree is constructed correctly without revealing user data. TAP supports a broad range of verifiable operations, including quantiles and sample standard deviations. We conduct a comprehensive evaluation of TAP, and compare it against two state-of-the-art baselines, namely IntegriDB and Merkle2, showing that the system is practical at scale.
Suzana Mesquita de Borba Maranhao Moreno, Jean-Marc Seigneur
The traditional way to prove someone’s address using formal documents like utility bills may not be feasible for some people, like those living in very poor neighborhoods, because they do not have these documents. In this paper, we propose an alternative way to prove someone’s address using a decentralized social trust solution. Because our design choices, this solution is able to work offline and does not need a logically centralized repository of all issued proof-of-address, in oppose to what would be achieved by using existing accretionary ID solutions. We validated this proposal by building a mobile application, using it in a real experiment in a Brazilian favela, and collecting mobile data. We also interviewed 20 people to complement our validation and help to guide the next steps of this work. The experiment showed that the solution is viable and easy to use. It is possible to adopt an approach like the one proposed to prove other facts, like gender, sex and income. These proofs may be used for different initiatives, like social programs, purpose-driven lending or other decentralized finance services.
There are billions of houses, businesses, and lands in the world, and we can prove the ownership of these assets with title deeds prepared by government offices. In the purchase and sale transactions of these titled assets, it is necessary to go through long and complex possess, and the actions that need to be taken do not end here. The asset must also be insured and paid regularly for insurance, tax, and some subscriptions like electricity, water, natural gas, etc. This study aims to create a blockchain-based asset management system that uses NFTs (non-fungible tokens), smart contracts, and the Ethereum network.
Summary In the Covid‐19 pandemic, information about the medical equipment such as personal protective equipment, ventilators, testing kits, oxygen cylinders, ICU beds, and patient diagnostic status is a black box for the patients. This article proposes a blockchain‐assisted Covid‐19 big data chain (CovidBChain) framework to handle the Covid‐19 data, which is of colossal size (volume), coming from different sources (variety) and generated at every time instance (velocity). CovidBChain is proposed to protect electronic health records and Covid‐19 equipment's information from illegal modification. CovidBChain provides transparency, access control, and integrity to Covid‐19 data. The status of critical equipment like ventilator, Covid‐19 beds, oxygen cylinder, and ICU status each such operation is integrated into the CovidBChain as a transaction. A prototype has been simulated using Ganache, Metamask, InterPlanetary File System, and Reactjs. The comparative assessment using proof‐of‐work (PoW) and proof‐of‐authority (PoA) deduces that the upload and retrieval time in PoA is less than PoW, while the transaction cost is more in PoW. The overhead of message exchange communication is reduced by a factor of in PoA as compared to the PoW approach. CovidBChain has been tested on the Ethereum official test network Ropsten for PoW and Goerli for PoA.
The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.
Smart healthcare systems provide user-centric medical services to patients based on collected information of patients inducing personal health information (PHI) and personal identifiable information (PII). The information (PII and PHI) flows into the smart healthcare system with or without any regulation and patient concern with the help of new information and communication technologies (ICT). The use of ICT comes with the security and privacy issues of collected PII and PHI data. The Europe Union has published the General Data Protection Regulation (GDPR) to regulate the flow of personal information. Towards this end, this paper proposes a blockchain-based data storage and sharing framework for a smart healthcare system that complies with the “Privacy by Design” rule of the GDPR. The personal information collected from patients is stored on off-chain storage (IPFS), and other information is stored on the blockchain ledger, which is visible to all participants. The smart contracts are designed to share the PII data with another participant based on prior permission of the data owner. The proposed framework also includes the deletion of PII and PHI in the system as per the “Right to be Forgotten” GDPR rule. Security and privacy analyses are performed for the framework to demonstrate the security and privacy of data while sharing and at rest. The comparative performance analysis demonstrates the benefit of the proposed GDPR-compliant data storage and sharing framework using blockchain. It is evident from the reported results that the proposed framework outperforms the state-of-the-art techniques in terms of performance metrics in a smart healthcare system.
A major issue for many applications of blockchain technology is the tension between immutability and compliance to regulations. For instance, the GDPR in the EU requires to guarantee, under some circumstances, the right to be forgotten. This could imply that at some point one might be forced to delete some data from a locally stored blockchain, therefore irreparably hurting the security and transparency of such decentralized platforms. Motivated by such data protection and consistency issues, in this work we design and implement a mechanism for securely deleting data from Bitcoin blockchain. We use zero-knowledge proofs to allow any node to delete some data from Bitcoin transactions, still preserving the public verifiability of the correctness of the spent and spendable coins. Moreover, we specifically use STARK proofs to exploit the transparency that they provide. Our solution, unlike previous approaches, avoids the complications of asking nodes to reach consensus on the content to delete. In particular, our design allows every node to delete some specific data without coordinating this decision with others. In our implementation, data removal can be performed (resp., verified) in minutes (resp., seconds) on a standard laptop rather than in days as required in previous designs based on consensus.
The key challenges of current blockchain, such as low throughput, high latency, and poor scalability, hinder its future development. The recent advances in artificial intelligence (AI) can well overcome these challenges, thus spurring an increasing number of organizations and individuals to equip blockchain with AI, making blockchain become more intelligent. However, AI behaviors need to be supervised forex-postforensics in case of dispute and accountability. This motivates us to envision a model to record the AI behaviors in intelligent blockchain. In this article, we propose such a model called AI-Tracer, which leverages proxy re-encryption based on Schnorr signature as well as InterPlanetary File System (IPFS) to be integrated into intelligent blockchain. AI-Tracer generates AI-digest during the AI learning process and encrypts it with proxy re-encryption. IPFS is responsible to store the encrypted AI-digest and returns a hash pointer related to AI-digest for further verification. The authorized user can verify the validity of AI-digest by leveraging advanced features of blockchain technology and only the valid AI-digest can be uploaded to intelligent blockchain, which implements trusted tracking for AI behaviors. AI-Tracer achieves fine-grained access control of AI-digest via proxy re-encryption. A user can retrieve the plaintext of AI-digest from intelligent blockchain with specific authorization. We conduct theoretical analysis to indicate the privacy and security of AI-Tracer. Moreover, we deploy AI-Tracer on the Hyperledger Fabric and demonstrate the efficiency and effectiveness of AI-Tracer through extensive experiments.
At present, secure multi-party computing is an effective solution for organizations and institutions that want to derive greater value and benefit from the collaborative computing of their data. Most current secure multi-party computing solutions use encryption schemes that are not resistant to quantum attacks, which is a security risk in today’s quickly growing quantum computing, and, when obtaining results, the result querier needs to collect the private keys of multiple data owners to jointly decrypt them, or there needs to be an interaction between the data owner and the querier during the decryption process. Based on the NTRU cryptosystem, which is resistant to quantum computing attacks and has a simple and easy-to-implement structure, and combined with multi-key fully homomorphic encryption (MKFHE) and proxy re-encryption, this paper proposes a secure multi-party computing scheme based on NTRU-type multi-key fully homomorphic proxy re-encryption in the blockchain environment, using the blockchain as trusted storage and a trusted execution environment to provide data security for multi-party computing. The scheme meets the requirements of being verifiable, conspiracy-proof, individually decryptable by the querier, and resistant to quantum attacks.
Zhe Tu, Huachun Zhou, Kun Li, Haoxiang Song · 5 authors
The rapid development of the Internet of Things (IoT) has dramatically increased the number of distributed IoT devices and users. Trust and Reputation Model (TRM) is a well-known technique for improving the security of IoT, which detects malicious attacks by evaluating user behavior. Since traditional distributed TRMs lack secure and reliable data sharing mechanisms, some works have integrated the TRMs into the trusted blockchains. Nevertheless, they have not realized the security requirements of the comprehensive assessment of user behavior and dynamic evaluation of reputation. Therefore, this paper introduces a Blockchain-based Trust and Reputation Model (BTRM), which evaluates user reputation from many aspects and can resist multiple malicious attacks in the distributed network. Second, we propose a novel Dynamic Evaluation Mechanism (DEM), which reduces the number of reputation evaluations without degrading network security and builds a trusting foundation between long-term inactive users and the network. Eventually, we deploy the proposed model DEM-BTRM in a prototype system of Hyperledger Fabric and compare it with existing reputation evaluation methods. The results show that the DEM-BTRM can comprehensively evaluate user behavior and dynamically detect malicious attacks.
Central bank digital currencies require the implementation of eKYC to verify whether a trading customer is eligible online. When an organization issues an ID proof of a customer for eKYC, that proof is usually achieved in practice by a hierarchy of issuers. However, the customer wants to disclose only part of the issuer's chain and documents to the trading partner due to privacy concerns. In this research, delegatable anonymous credential (DAC) and zero-knowledge range proof (ZKRP) allow customers to arbitrarily change parts of the delegation chain and message body to range proofs expressed in inequalities. That way, customers can protect the privacy they need with their own control. Zero-knowledge proof is applied to prove the inequality between two time stamps by the time stamp server (signature presentation, public key revocation, or non-revocation) without disclosing the signature content and stamped time. It makes it possible to prove that the registration information of the national ID card is valid or invalid while keeping the user's personal information anonymous. This research aims to contribute to the realization of a sustainable financial system based on self-sovereign identity management with privacy-enhanced PKI.
Ahmed S Alardawi, Ammar Odeh, Abobakr Aboshgifa, Nabil Belhaj
Federated Learning (FL) is a machine learning framework that allows collaborative model training across multiple decentralized edge devices or servers while keeping the data local and private. This method eliminates the need to exchange sensitive data, enhancing privacy and security. FL leverages the distributed nature of data generation for more effective learning. The paper explores the foundational concepts of FL, focusing on how it enables collective learning without centralizing individual data. This is particularly important in sectors where data privacy is critical, such as healthcare and finance. It discusses the technical mechanisms of FL, including the algorithms for decentralized training, data aggregation techniques, and the role of a central server, when applicable. In FL, devices compute on local data and share only model updates or gradients, reducing the risk of exposing sensitive information. FL has diverse applications, such as enabling disease diagnosis models in healthcare without sharing patient data and enhancing privacy in mobile device personalization. However, FL also faces challenges, including issues with synchronization of updates, handling non-IID (independent and identically distributed) data across devices, and ensuring resistance to adversarial attacks. The paper addresses these challenges and examines strategies to overcome them, providing a comprehensive overview of FL's potential and its future applications [1, 2]. Keywords: Federated Learning, Privacy, Security, Decentralized, Machine Learning, Edge Devices
Rasel Iqbal Emon, Md. Mehedi Hassan Onik, Abdullah Al Hussain, Toufiq Ahmed Tanna · 7 authors
A distributed ledger technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Additionally, edge technology is trending rapidly which brings the computing and data storing facility closer to the user end (device), in order to overcome network bottlenecks. This study, therefore, utilises the transparency, security, efficiency of blockchain technology along with the computing and storing facility at the edge level to establish privacy preserved storing and tracking schemes for electronic health records (EHRs). Since the EHR stored in a block is accessible by the peer-to-peer (P2P) nodes, privacy has always been a matter of great concern for any blockchain-based activities. Therefore, to address this privacy issue, multilevel blockchain, which can enforce and preserve complete privacy and security of any blockchain-based application or environment, has become one of the recent blockchain research trends. In this article, we propose an EHR sharing architecture consisting of three different interrelated multilevel or hierarchical chains confined within three different network layers using edge computing. Furthermore, since EHRs are sensitive, a specific data de-identification or anonymisation strategy is also applied to further strengthen the privacy and security of the data shared.
According to the ever-growing supply and demand of IoT content, IoT big data in diversified applications are deemed a valuable asset by private and public sectors. Their privacy protection has been a hot research topic. Inspired by previous work on bounded-error-pruned IoT content market, we observe that the anonymity protection with robust watermarking can be developed by further pruning data for better resource-efficient IoT big data without violating the required quality of sensor service or quality of decision-making. In this paper, resource-efficient anonymity protection with watermarking is thus proposed for data consumers and owners of IoT big data market via blockchain. Our proposed scheme can provide the IoT data with privacy protections of both anonymity and ownership in IoT big data market with resource efficiency. The experiments of four different-type IoT datasets with different settings included bounded-errors, sub-stream sizes, watermark lengths, and ratios of data tampering. The performance results demonstrated that our proposed scheme can provide data owners and consumers with ownership and anonymity via watermarking the IoT big data streams for lossless compressibility. Meanwhile, the developed DApp with our proposed scheme on the Ethereum blockchain can help data owners freely share and trade with consumers in convenience with availability, reliability, and security without mutual trust.
Open access
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Public healthcare has transformed from treatment to preventive care and disease management. The Common Data Model (CDM) provides a standard data structure defined to utilize hospitals’ data. Digital identity takes a significant role as the body of information about an individual used by computer systems to identify and establish trust among organizations. The CDM research network, composed of users handling medical information, has several digital identities associated with their activity. A high central authority cost can be reduced by Distributed Ledger Technology (DLT). It enables users to control their identities independently of a third party. To preserve the privacy of researchers in clinical studies, secure identification is the main concern of identifying the researcher and its agents. To do so, they should pose a legally verifiable credential in the cloud CDM. By presenting the proof represented by the capability that the user has, each identity has access control that is linked to an authentication credential that the cloud CDM can verify. Assurance in one’s identity is confirmed by asserting claims with the identity and its capability, providing its verifiable credential to the authentication entity in the cloud CDM. This paper describes the user-centric claim-based identity operation model based on use cases to handle researcher identity in the cloud CDM. In this model, credentials are designed as a capability and presented to them to access SPs in the cloud CDM. To provide well-controlled access control in the cloud CDM, we build and prototype a capability based CDM management system.
The Internet of Things (IoT) and blockchain technologies characterizing the era of the fourth industrial revolution have enabled smart home networks to support their various systems and services. In a blockchain-based smart-home network environment, all connected IoT devices must be controlled safely and efficiently. Nevertheless, existing block-chain-based smart-home IoT systems pose a delay issue due to the necessary block generation time. In addition, IoT devices installed in smart homes should be able to prevent forgery attacks such as spoofing because they are often directly associated with personal information. In this study, we proposed an enhanced method to control smart home devices safely and efficiently by applying the zero-knowledge proof combined with a blockchain-based IoT system to protect the public keys of home network devices and the communication among them. The proposed model was approximately 10 s faster than the block generation-based model when it communicated three times in rinkeby, which is one of the test networks of Ethereum.
Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.
With the advances of the Internet of Things (IoT) and 5G/6G wireless communications, the paradigms of mobile computing have developed dramatically in recent years, from centralized mobile cloud computing to distributed fog computing and mobile edge computing (MEC). MEC pushes compute-intensive assignments to the edge of the network and brings resources as close to the endpoints as possible, addressing the shortcomings of mobile devices with regard to storage space, resource optimisation, computational performance and efficiency. Compared to cloud computing, as the distributed and closer infrastructure, the convergence of MEC with other emerging technologies, including the Metaverse, 6G wireless communications, artificial intelligence (AI), and blockchain, also solves the problems of network resource allocation, more network load as well as latency requirements. Accordingly, this paper investigates the computational paradigms used to meet the stringent requirements of modern applications. The application scenarios of MEC in mobile augmented reality (MAR) are provided. Furthermore, this survey presents the motivation of MEC-based Metaverse and introduces the applications of MEC to the Metaverse. Particular emphasis is given on a set of technical fusions mentioned above, e.g., 6G with MEC paradigm, MEC strengthened by blockchain, etc.
Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new federated learning framework which updates model from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT). SDAGFL has the advantage of personalization, resisting single point of failure and poisoning attack in fully decentralized federated learning. Because of these advantages, the SDAGFL is suitable for the federated learning in IoT scenario where the device is usually battery-powered. To promote the application of SDAGFL in IoT, we propose an energy optimized SDAGFL based event-triggered communication mechanism, called ESDAGFL. In ESDAGFL, the new model is broadcasted only when it is significantly changed. We evaluate the ESDAGFL on a clustered synthetically FEMNIST dataset and a dataset from texts by Shakespeare and Goethe's works. The experiment results show that our approach can reduce energy consumption by 33\% compared with SDAGFL, and realize the same balance between training accuracy and specialization as SDAGFL.
Zero-Knowledge Contingent Payment presents how Bitcoin contracts can provide a solution for the so-called fair exchange problem.Banasik, W. et al. first presented an efficient ZeroKnowledge Contingent Payment protocol for a large class of NP-relations, which is a protocol for selling witness. It obtains fairness in the following sense: if the seller aborts the protocol without broadcasting the final message then the buyer finally gets his payment back. However, we find that the seller in the protocol could refuse to broadcast the final signature of the transaction without any compensation for the buyer. As a result, the buyer cannot get the witness from the final signature of the transaction and has the payment for the witness locked until finishing the large computation for a secret signing key. In this paper, we fix this problem by augmenting the efficient Zero-Knowledge Contingent Payment protocol. We present a new protocol where the seller needs to provide the deposit before the zero-knowledge proof of knowledge of the witness being sold. And then the buyer could obtain the seller's witness if the seller broadcasts the final signature of the transaction and gets the payment and his deposit. Otherwise, the buyer could get back the payment and obtain the seller's deposit. This new augmented protocol is constructed without any new assumptions.
Shuhao Zheng, Yanxi Lin, Yang Yu, Ye Yuan · 6 authors
Auditing the information leakage of latent sensitive features during the transborder data flow has attracted sufficient attention from global digital regulators. However, there is missing a technical approach for the audit practice due to two technical challenges. Firstly, there is a lack of theory and tools for measuring the information of sensitive latent features in a dataset. Secondly, the transborder data flow involves multi-stakeholders with diverse interests, which means the audit must be trustless. Despite the tremendous efforts in protecting data privacy, an important issue that has long been neglected is that the transmitted data in data flows can leak other regulated information that is not explicitly contained in the data, leading to unaware information leakage risks. To unveil such risks trustfully before the actual data transfer, we propose FIAT, a Fine-grained Information Audit system for Trustless transborder data flow. In FIAT, we use a learning approach to quantify the amount of information leakage, while the technologies of zero-knowledge proof and smart contracts are applied to provide trustworthy and privacy-preserving auditing results. Experiments show that large information leakage can boost the predictability of uninvolved information using simple machine-learning models, revealing the importance of information auditing. Further performance benchmarking also validates the efficiency and scalability of the FIAT auditing system.
Electronic medical records (EMRs) are extremely private data in the medical industry. Clinicians use the patient data that the EMR stores to quickly assess a patient's status and save diagnostic information. In the conventional medical model, it is easy for duplicate exams, medical resource waste, or the loss of medical records to happen when a patient is transferred between several medical facilities due to problems with data sharing and exchange, inadequate data privacy, security, confidentiality, and difficulties with data traceability. This paper recommends a Hyperledger Fabric-based strategy to promote the exchange of EMR models. With the use of Hyperledger Fabric, EMR stakeholders can be brought into the channel to facilitate data sharing. Attribute-based access control (ABAC) allows users to design the data access control policy, and the data access control may improve security. Any record stored in the blockchain can be viewed using the Hyperledger Fabric feature and it cannot be altered or destroyed, ensuring data traceability. Through proxy re-encryption, which makes sure that the data is not leaked during data exchange, data secrecy can be ensured. A module for medical tokens has now been added. Many foreign medical institutions currently use the medical token system, and the system described in this paper can use the tokens to pay for some medical expenses. The tokens are obtained by the patient's initiative to share their EMR with the medical institution for research, which is how many foreign medical institutions currently use the medical token mechanism. This paradigm can encourage the growth of medical data by enabling stakeholders to collaborate and share EMR trust.