Djilali Moussaoui, Benamar Kadri, Mohammed Feham, Boucif Ammar Bensaber
Among the important challenges for Vehicular Adhoc Network (VANET) Security and Privacy. Most of the solutions for privacy in vanet are based on pseudonyms. And the pseudonyms are digital certificates with very limited information, valid for a short time, and hide the identity of the vehicle. The pseudonym operations (issuing, changing, and revoking) are centralized by a certification authority. In our paper, we propose a fully distributed management for pseudonyms in VANETs. We use the blockchain technology to perform the different operations related to pseudonyms. Our proposal use two blockchains, one for registering pseudonyms, the second for revoked ones. The vehicles are considered as miners in the blockchain. Our approach makes the vehicles in VANET more autonomous in managing the security, and reduce the exchanged data with a centralized authority (Certificate Authority-CA).
With the rapid development and widespread application of crowdsourcing, the limitations of traditional systems are gradually exposed. First, traditional systems fail to protect the privacy of task requesters and workers. They typically rely on a centralized server to aggregate the task content and workers' interests, while these data contain sensitive information. Second, crowdsourcing resources in each system are isolated. The tasks in one system cannot reach potential workers in other systems. Thus, there is a great need to build a new privacy-preserving and federated crowdsourcing system. However, the existing privacy-preserving solutions rely on a trusted third party to perform key management, which is not applicable in a federated setting. To this end, we propose the first proxy-free privacy-preserving and federated crowdsourcing system. It interconnects the existing crowdsourcing systems and can perform encrypted task matching across various systems without relying on a trusted third-party authority. Our main idea is to achieve federated crowdsourcing by moving secure task matching to the trusted smart contract. To get rid of the dependence on the trusted authority, we combine the rewritable deterministic hashing technique with searchable encryption schemes to achieve secure on-chain task-matching authorization. Moreover, we utilize the puncturable encryption technique to implement secure authorization revocation. We formally analyze the security of our design and implement a prototype on Ethereum. Evaluation results demonstrate that our design is secure and efficient for blockchain-based crowdsourcing.
Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
Open access
2 source records
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Mallikarjun Reddy Dorsala, V. N. Sastry, Chapram Sudhakar
With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability – The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments – The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.
With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform.
To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.
Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.
The digitization, informatization, and intelligentization of physical systems require strong support from big data analysis. However, due to restrictions on data security and privacy and concerns about the cost of big data collection, transmission, and storage, it is difficult to do data aggregation in real-world power systems, which directly retards the effective implementation of smart grid analytics. Federated learning, an advanced distributed learning method proposed by Google, seems a promising solution to the above issues. Nevertheless, it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed. Thus, we here propose a serverless distributed learning platform based on blockchain to solve the above two issues. In the proposed platform, the task of machine learning is performed according to smart contracts, and encrypted models are aggregated via a mechanism of knowledge distillation. Through this proposed method, a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios. Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.
P. Chinnasamy, B. Vinodhini, V. Praveena, C. Vinothini · 5 authors
Abstract Internet-of-Things and Blockchain technology are evolving all around today’s modern world to solve many problems such as security, communications, data collection and analysis, etc. However, we also have some issues, such as efficient data sharing, restriction of access, reliable authentication, etc. The proposed framework is designed to solve the problems related to security and authorization in IoT network access control. In addition, the system’s aim is to accomplish security, authorizing, and encryption for information exchange through IoT networks. In this article, a novel system is introducing to provide the data sharing system that integrates blockchain based access control system for IoT devices. Here, we are creating three different smart contracts to offers an efficient access control management like contract to provide access control, contract to provide authentication, and contract to provide a judgment. Finally, the effectiveness of the proposed approach is measured against the cost consumption of smart contracts and cryptographic functions. The cost utilization comparison is carried out against certain current approaches as well as the findings clearly demonstrate that the proposed approach is cost-effective.
A rapid-growing machine learning technique called federated edge learning has emerged to allow a massive number of edge devices (e.g. smart phones) to collaboratively train globally shared models without revealing their private raw data. This technique not only ensures good machine learning performance but also maintains data privacy of the edge devices. However, the federated edge learning still faces the following critical challenges: (i) difficulty in avoiding unreliable edge devices acting as workers for federated edge learning, and (ii) lack of efficient learning task assignment schemes among task publishers and workers. To tackle these challenges, reputation is utilized as a metric to evaluate the trustworthiness and reliability of the edge devices. A many-to-one matching model is proposed to address the task assignment problem between task publishers and reliable workers with high reputation. For stimulating reliable edge devices to join model training and enable secure reputation management, blockchain is employed to store the training records and manage reputation data in a decentralized and secure manner without the risk of a single point of failure. Numerical results show that the proposed schemes can achieve significant performance improvement in terms of reliability of federated edge learning.
Over the past decade, blockchains and distributed ledger technologies have rapidly evolved. With increasing transaction volumes and the proliferation of decentralized applications based on smart contracts, a need for a deeper understanding arises. We structure the field that we term distributed ledger analytics.
Wenjun Fan, Jinoh Kim, Ikkyun Kim, Xiaobo Zhou · 5 authors
Bitcoin and cryptocurrency rely on peer-to-peer (P2P) networking. Incorporating intelligence on Bitcoin miners to analyze and control networking can improve the information delivery and defend against networking threats. However, applying machine learning (ML) for building intelligence introduces a challenge because miners participate in the resource-intensive distributed consensus protocol and the ML application can consume much computing resources. In this paper, we study the feasibility and the interplay between ML algorithms and mining operations. Our prototype-based experiments measure and compare the performance of the ML algorithms to evaluate the implementation overhead and efficiencies of the ML algorithms and their impacts on mining operations, i.e., the mining reduction when the ML algorithm is running in parallel.
The development of educational informatization makes data privacy particularly important in education. With society's development, the education system is complicated, and the result of education evaluation becomes more and more critical to students. The evaluation process of education must be justice and transparent. In recent years, the Onscreen Marking (OSM) system based on traditional cloud platforms has been widely used in various large-scale public examinations. However, due to the excessive concentration of power in the existing scheme, the mainstream marking process is not transparent, and there are hidden dangers of black-box operation, which will damage the fairness of the examination. In addition, issues related to data security and privacy are still considered to be severe challenges. This paper deals with the above problems by providing secure and private transactions in a distributed OSM assuming the semi-trusted examination center. We have implemented a proof-of-concept for a consortium blockchain-based OSM in a privacy-preserving and auditable manner, enabling markers to mark on the distributed ledger anonymously. We have proposed a distributed OSM system in high-level, which provides theoretical support for the fair evaluation process of education informatization. It has particular theoretical and application value for education combined with blockchain.
Storing and processing huge amount of private data is a challenging problem. The problem becomes, in particular, interesting if the user-side storage and computational power is limited. One way to solve the problem of outsourcing private data and maintaining an access control on the storage is proposed by using blockchain technology. However, blockchain technology requires heavy computational power and machinery. In this paper, we propose an approach to store and sell private data with the help of secret sharing. In comparison to blockchain, our methodology is simpler and preserves the privacy of stored data.
Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao · 7 authors
In this article, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain's security and performance.
With the emergence of computation-intensive vehicular applications, computation offloading based on mobile-edge computing (MEC) has become a promising paradigm in resource-constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that in this article, a blockchain-based secure computation offloading scheduling scheme is proposed. It embraces the blockchain-based trust management paradigm and smart contract-enabled deep reinforcement learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, roadside unit (RSU) blockchain, and cloud blockchain. Furthermore, a smart contract-enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.
Badda Chandra Vardhini, Shreyas N Dass, R Sahana, R. Chinnaiyan
The common issues in medical services within the country are mostly associated with doctors' referral process, data transfer between health institutions, and portals for patients to access their medical information. Specific issues arise, such as sharing health Records across institutes or hospitals, problems with misuse of data once shared, no security, etc. The Electronic Health Record (EHR) Framework on Blockchain addresses those issues, resulting from a collaboration of all stakeholders involved. This paper explores the likelihood of representing medical records to make sure data privacy, data accessibility, and data interoperability for the healthcare-specific scenario. Data privacy refers to affording protection to ensure data is available when needed and not used, imparted, accessed, altered, or deleted while being stored or retrieved, or transmitted. Data accessibility is the ability to access the data regardless of natural or artificial accidents, hardware, or others. Improving the accessibility of health data in the healthcare sector while ensuring privacy has been identified as a necessary capability that involves every individual and organization. Traditionally, healthcare interoperability has centered on sharing data between business institutions, such as various hospital systems. The emphasis has lately been on patient-driven information sharing, where the exchange of medical information is patient-mediated and patient-driven. We propose implementing a large-scale information infrastructure to access Smart Contracts sponsored by EHRs as information mediators. The decentralized nature of blockchain technology will aid in making the EHR accessible over a broader network. Using Blockchain will help make far-reaching changes in the healthcare industry by providing immutable, authentic, and accessible medical records, privacy, and faster payments.
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.
Yunguo Guan, Hui Zheng, Jun Shao, Rongxing Lu · 5 authors
Due to the big data blowout from the Internet of Things and the rapid development of cloud computing, outsourcing computation has received considerable attention in recent years. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis and machine learning. However, none of those schemes are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and the worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, in this article, we propose a new outsourcing polynomial computation scheme by employing a variant of Horner’s method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and the worker if the result from the worker can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we also implement a prototype of our proposal and run it on an Ethereum test net. The extensive experimental results demonstrate that our proposal is efficient in terms of computational cost.