Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people's lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security.
Federated learning can effectively alleviate the data privacy problem of the participants, but the parameters or gradients passed in the model training may still leak the private data of the participants. Worse, aggregation server may return fake aggregation results. Existing solutions either use complex cryptographic primitives such as zero-knowledge proofs, or require interaction among participants, causing them high computation or communication overhead. Therefore, this paper proposes a secure and verifiable federated learning (SVFL) scheme. Specifically, SVFL performs privacy protection by introducing noise that can be offset during the aggregation process, and utilizes linear homomorphic hash to verify the correctness of the aggregation results. Compared with existing schemes, SVFL hardly loses accuracy due to the introduced security mechanism, and has low computation and communication overhead. Experimental results show that the performance of SVFL is almost consistent with the original federated learning without any protection, which makes SVFL applicable to edge devices. The computation and communication overhead of SVFL does not increase with the number of participants, which makes SVFL applicable to high-concurrency scenarios.
Abstract The goal of a content delivery network (CDN) is to reduce the content delivery latency to end-users by using distributed cache servers. Nevertheless, it is very expensive to deploy and maintain cache servers in a large-scale. To solve this problem, CDN providers have come up with a new content delivery strategy: allowing end-users's IoT edge devices to share their storage/bandwidth resources. This new edge CDN platform needs to address two core questions: (1) how can we incentivize end users to share IoT devices? (2) how can we facilitate a safe and transparent content transaction environment for end users? In this paper, we introduce SmartSharing, a new content delivery network solution. In smartSharing, the over-the-top (OTT) IoT devices belonging to end-users are used as mini-cache servers. To motivate end users to share the idle devices and storage/bandwidth resources, SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms (to be specific, a tailored Expectation-Maximization (EM) algorithm). To facilitate content trading among end users, SmartSharing creates a secure and transparent transaction platform based on smart contracts in Ethereum. In addition, SmartSharing's performance evaluation not only through trace-driven simulations in the real world, but also a prototype using content metadata and the achieved pricing schemes. The evaluation results show that CDN providers, end users and content providers can all benefit from our SmartSharing framework.
Jingwei Liu, Yue Fan, Rong Sun, Lei Liu · 6 authors
Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information, such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multikeyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multikeyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks.
The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this article, we propose a new auditable redactable blockchain (RB) scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the RB and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.
The Internet of Things has been applied in various fields of industry, which has promoted the intelligent development of the industry and improved efficiency in industrial production. The devices involved in the IoT have generated useful and sensitive data over time and upload the data to the cloud to realize real-time data sharing. To ensure the confidentiality of data, many systems use attribute-based encryption primitive to encrypt data. However, there are still some security and privacy problems in this mode, such as the lack of identification of malicious users who leaked private keys, performance bottleneck caused by excessive reliance on a single central authority (CA), and vulnerability because a single CA holds the private keys of all users in the system. In this article, white-box tracking is used to identify malicious users. The alliance chain is introduced to support multiauthority environments, where the consensus nodes are managed by different authorities and assist CA in generating partial private keys. To protect users’ privacy, users remain anonymous at all times during their interactions with blockchain consensus. The security analysis and simulation results show that the proposed scheme outperformed other comparable schemes, indicating that it is a preferable scheme.
Long Zhang, Gang Feng, Shuang Qin, Xiaoqian Li · 6 authors
Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.
The spread of infectious diseases in crowded spaces such as shopping malls, markets, and hospitals is a growing concern. In order to mitigate this risk, it is crucial to develop a method that leverages the power of distributed crowd to learn, de- tect, and alert individuals about potential health hazards. Hence, the integration of federated learning (FL), and blockchain (BC) to provide intelligent platforms that facilitate pervasive AI and trust amongst IoT devices and smart phones can play a significant role in achieving this goal. In this study, we propose a new technique named BC-FL Location-Based, which utilizes smart applications installed on IoT devices and smart phones to detect and predict imminent health risks. The technique works by using algorithms such as maximal clique to detect individuals in close proximity and sharing their health data through a blockchain network. A smart contract then triggers a node with sufficient resources to gather users' learning experiences from the blockchain, aggregate it, and run a model to determine if any of the individuals present in the area are infected. To demonstrate the effectiveness of the proposed technique, we conducted simulation experiments using Ethereum-based private blockchain network, where nodes represent individuals in different locations. We used the maximal clique algorithm to simulate the movement of individuals and compared the results of the model run on individual data versus aggregated data. Experiments showed promising results, with accuracy of detection increasing to 99% when using iid data and 90% when using non-iid data.
The development of the Metaverse is completely changing how business is done in the physical world. The Metaverse considerably improves intelligent manufacturing by mapping out operations and spreading them into virtual space. The Metaverse can access data from numerous production and operation lines thanks to the Internet of Things (IoT), enabling efficient data analysis and decision-making. However, the problem of sharing sensitive and private data remains a challenge when integrating the Metaverse with IoT. Federated learning (FL) has emerged as a distributed machine learning (ML) setting that can overcome the security problems related to data sharding With FL, several devices can work together to create an ML model under the direction of a central server while maintaining the privacy and security of their local training data. FL in the Metaverse continues to face significant challenges due to a lack of transparency, learning forgetting caused by streaming industrial data, and problems with non-independent and identically dispersed (non-iid) data. In this paper, we develop a FL framework for transparent and secure model learning in the Metaverse using blockchain technology. The blockchain ledger stores and verifies the model updates which ensures that all updates are tamper-proof and transparent to all parties involved. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with reliable behavior priority. The numerical result demonstrates that our framework performed better on the chosen indicators.
Blockchain technology is poised to change nearly every facet of our digital lives. Blockchain can be said as an immutable, and decentralized database. Data stored in the blockchain cannot be tampered, making it secure. Also, being decentralized, no central entity controls the blockchain, ensuring reliability. So, the data can be stored publicly, such that anyone could read the data. This vast availability of data could enable data scientists to perform various analytics over the large amount of data. This could result in many useful insights in many fields. But, when sensitive data such as healthcare data and reports are to be stored in the blockchain, it could raise several privacy issues. Medical reports or personal information cannot be stored in a way that any one could access them. Thus, this paper suggests a way to store and perform analytics over sensitive data in blockchain. In this paper homomorphic encryption is used to store the sensitive data in blockchain. Computed results from homomorphic encryption on ciphertexts are encrypted. When the encrypted result is decoded, it produces a result that is identical to what would have happened if the operations had been carried out in plaintext. Thus, critical data are encrypted and stored in the blockchain, data analytics are performed over them, without knowing the actual data. Thus, the proposed system provides privacy of the data stored publicly and could also profit the data scientists with access to large amount of real time data directly from the owners of the data.
Xiaowan Wang, Huiyin Xie, Shan Ji, Liang Liu · 5 authors
The rapid development of the Internet and Internet of Things has rapidly introduced human society into the information age, and the way of fake news production has been updated, which has greatly affected the normal life of human beings. In order to identify worthless fake news and trace massive fake news data from unknown sources, and share valuable news data to fully disseminate effective real news, news owners usually store news data in cloud. Users of IoT terminals can access news data on demand without storing it locally. However, the authenticity of the fictive newspaper numbers source, which is easy to destroy, and the social media platform. Besides, when massive news data is saved on cloud server, the news owners have to at the risk of lose physical control over news data and it will face the risk of fake news being disseminated and real news being falsified. Thus, this paper proposes a novel mechanism for secure storage of news data using blockchain technology. Firstly, traceability and verification of fake news data is improved by the cooperative storage model on and off the chain. Secondly due to the inability of past polynomial commitment to update the commitment, we will be a hindrance to use polynomial commitment to build a secure authentication protocol. Therefore, in this paper, we design the update algorithm for polynomial commitment in order to be able to guarantee the consistency of on-chain and blockchain database news data.
Fengqun Wang, Jie Cui, Qingyang Zhang, Debiao He · 6 authors
In edge-assisted cross-domain Industrial Internet of Things (IIoT), blockchain-based authentication is an effective way to build cross-domain trust and secure cross-domain data. However, existing authentication schemes still have serious challenges in terms of efficiency and security. In this paper, we propose a blockchain-based lightweight message authentication scheme. First, to address efficiency challenges, we build a blockchain-enabled edge-assisted lightweight authentication framework. This framework uses edge servers to assist smart devices in achieving cross-domain authentication and effectively reduce redundant interactions between entities. Second, to resolve the security challenges, we design a lightweight message authentication algorithm for cross-domain IIoT. The algorithm guarantees message security with low computational overhead and is suitable for multi-receiver cross-domain IIoT. The security proof and analysis demonstrate that the proposed scheme is secure under the random oracle model and can resist various attacks. The performance evaluation shows that our proposed scheme is superior in terms of computation and communication overhead when compared with other related schemes.
Blockchain technology has become wide usage technology that can be integrated to variety of applications in different sectors to enhance the performance, security or to add a layer of implementation with specific features. In some cases it replaces the traditional type of existed systems to provide a solution to specific concerns such as the case with land record system. In this research, we provide a brief introduction of the blockchain technology in land administration, analyzing some of existed and proposed frameworks for land administration systems through a systematic review and summarize the results. Also, we highlighted the main benefits of the blockchain technology and the most important vulnerabilities in blockchain platforms. Also, we proposed a private blockchain framework using Hyper- ledger Fabric and highlighted the main reasons to choose such a platform for our system and how it can solve the double spending and tampering issues. Finally, the objective of this research is to provide mechanisms that solve the security issues to answer the research questions and develop and verify the effectiveness of our proposed framework.
The rapid digital revolution in recent decades has resulted in an overwhelming amount of information, particularly in the realm of modern education systems and related materials. This phenomenon, often referred to as information overload, necessitates the development of educational systems that can effectively search, classify, and categorize this vast amount of available information. Of utmost importance for such educational information systems is the safeguarding of personal data, which refers to information that can identify an individual or their family. School records, for example, contain various types of personal data such as the individual’s name, address, contact details, disciplinary history, as well as their grades and progress checks. Even if individuals choose to make this data public, it remains inherently personal. Another category of data involves more sensitive topics such as student biometrics (e.g. fingerprints, photographs), religious beliefs, health information (e.g. allergies), or dietary restrictions, which may imply religious or health-related aspects. Processing data in this category can pose risks to individuals; hence, strict rules and appropriate consent are necessary to ensure their protection. To address these challenges, this research paper proposes a zero-knowledge proof intelligent recommendation system designed to protect students’ data privacy in the digital age. The proposed method incorporates an Intelligent Recommendation System (IRS) that utilizes an optimized version of the Matrix Factorization technique, calculated as an Eulerian Walk chart. Furthermore, the Schnorr Zero-Knowledge Proof format, based on the discrete logarithm problem, ensures the privacy of personal data during message exchange between educational entities.
Zijian Bao, Debiao He, Huaqun Wang, Min Luo · 5 authors
Recently, with the rapid improvement of e-health technology, a large amount of precious medical data has been accumulated in different entities, such as hospitals, clinics, and medical institutions, promoting the development of data sharing in e-health services. However, most of them lacks fine-grained functionalities: selective linkability and traceability, which are critical in an e-health environment. Furthermore, we observe that existing schemes mostly rely on centralized storage centers, which will lead to a single point of failure and privacy disclosure. In this article, we first construct a group signature schemeSLTGSsuitable for a data sharing environment. It supports selectively linking two different message-signature pairs to the same signer. Further, it provides an algorithm to trace the signer. Then, based on theSLTGSscheme, we leverage distributed technology (i.e., interplanetary file system (IPFS) and blockchain) and attribute-based encryption to propose a distributed data sharing scheme. We claim that our scheme meets anonymity, accountability, linkability, traceability, fine-grained and efficient access control, and distributed storage. Moreover, the proposed data sharing scheme yields a practical performance making it suitable for e-health applications.
Web3, an emerging blockchain-based decentralized network, grants users ownership and enhances the collaboration among devices under monitoring. Benefiting from decentralization and in-memory computing, vehicular edge networks can process tasks such as road object detection distributedly without being attacked. Recently, to provide intelligent service for Web3 users, artificial intelligence applications have been booming, thus generating enormous deep learning models. These models are supposed to be deployed in the edge due to their massive computation. Further-more, edge servers may face overload and intolerable delay for the high concurrency of offloaded deep learning tasks. How to determine an optimal offloading decision in the highly dynamic and heterogeneous edge-cloud environment is still a challenge. To tackle the mentioned challenge, a dynamic offloading strategy based on game theory combined with convolutional neural network (CNN) partition for vehicular edge networks, named GPOV, is proposed. Specifically, CNN partition can utilize resources more efficiently and reduce the delay with parallelism. The game theoretic offloading decision strategy can determine the optimal offloading policy according to the real-time environment. The performance of our strategy is validated in the final part of this article.
With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications.
Since numerous firms and governments are in dire need of safeguarding their premises, the confidentiality of data is of utmost importance in achieving this goal. The security of facility entry is crucial, but it's also crucial for future demands that the entrance logs are auditable. Due to these reasons, restricted locations require access control and round-the-clock management. A centralized approach for administering grants and monitoring access is a simple choice as it is among the most frequently employed ones. In this respect, airports are deemed as being one of the safest places since every airport provides certain public and security services and accession to these restricted facilities must be controlled and authorized by the airport management organization. Unchangeable, auditable and always-available access records and open management in accordance with its confidentiality necessities are all answers to the issues experienced in the sector with regard to access control. In this work, a role-based access control strategy for regulating airport entrances that are scattered and require government oversight is presented and implemented using the Ethereum blockchain. Our obtained results demonstrate the adaptability and also utility of our proposal for other restricted facilities that have similar requirements.
The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.