A Blockchain is a trustworthy, immutable, decentralized database system that functions in different nodes. During the age of decentralized networks and large datasets, the confluence of Blockchain with Intelligent Machine technologies nowadays emerged as an excellent solution to provide robust, transparent, & private transactions. While the individual merits of Blockchain in ensuring data integrity and ML in deriving insights are well-established, their synergistic effects particularly in the realm of privacy preservation are yet to be fully explored. This fusion has the potential to revolutionize sectors like health care, finance, and supply chain by offering unprecedented levels of data privacy without compromising on system performance. This paper presents a comprehensive review of existing models that employ machine learning techniques for privacy preservation operations within blockchain frameworks. The paper contributes to the academic discourse by laying down a foundational framework for understanding and selecting the most appropriate blockchain-ML models for privacy preservation. The insights derived from this work are instrumental in steering the future development and deployment of secure, efficient, and privacy-preserving solutions across various industry verticals & scenarios.
Zhang Lejun, Peng Minghui, Shen Su, Weizheng Wang · 9 authors
With the rapid development of information technology, IoT devices play a huge role in physiological health data detection. The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes. The storage capacity of edge nodes close to users is limited. We should store hotspot data in edge nodes as much as possible, so as to ensure response timeliness and access hit rate; However, the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data; How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem. Our paper proposes a redundant data detection method that meets the privacy protection requirements. By scanning the cipher text, it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data. It has the same effect as zero-knowledge proof, and it will not reveal the privacy of users. In addition, for redundant sub-data that does not meet the requirements of hot data, our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data. We use Content Extraction Signature (CES) to generate the remaining hot data signature after the redundant data is deleted. The feasibility of the scheme is proved through safety analysis and efficiency analysis.
Статья посвящена актуальной проблеме обеспечения конфиденциальности в системах распределенного реестра. Рассматривается прикладная задача обеспечения конфиденциальности данных при операциях с цифровыми финансовыми активами. Представлено сравнение различных методов обеспечения конфиденциальности, включая перемешивающие сети, кольцевые подписи и оффчейн-протоколы. Отмечено, что эти методы не достигают достаточного уровня децентрализации, что является важным аспектом для систем распределенного реестра. Для одновременного обеспечения свойств децентрализации и конфиденциальности информации используются методы доказательства с нулевым разглашением, включая методы компактных неинтерактивных доказательств знания (SNARK). В статье приводится математическая модель систем доказательства SNARK, а также описаны подходы к их программной реализации. Приведены результаты экспериментов, направленные на сравнение производительности методов SNARK для решения прикладной задачи проведения операций с цифровыми финансовыми активами. Результаты эксперимента позволяют выделить дальнейшие возможности снижения времени генерации доказательства и сокращения его объема посредством использования пакетной верификации. Полученные результаты имеют практическую значимость для разработки систем распределенного реестра, требующих высокого уровня конфиденциальности и децентрализации.
Industrial big data has experienced from data silos due to its high potential value and strong security requirements, making it difficult to share securely across domains. Blockchain-based solutions allow nodes to establish access control to trusted data on unreliable or trustless networks, but still face issues such as inefficient data sharing and leakage of sensitive information. In this paper, we propose a blockchain-based access control scheme for privacy security and dynamic regulation. First, ciphertext policy attribute-based encryption (CP-ABE) is developed to gain fine-grained access to node resources, with verifiable outsourcing decryption method to significantly reduce computational pressure on end users. Second, a policy hiding method based on multi-chain architecture is proposed, which performs double hiding of attribute information and access policy information on the blockchain. Finally, a supervisory policy that incorporates dynamic trust assessment and smart contracts is proposed to achieve effective detection and hierarchical classification punishment of malicious behavior. Security analysis and experimental results show that our scheme can limit the complexity of terminal decryption at a constant level and effectively achieve secure and efficient access control in the industrial Internet environment.
Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.
Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.
As the usage of artificial intelligence (AI) grows within the field of cybersecurity, so too does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology (BT) has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of BT with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of BT and decentralized AI for cybersecurity serving as the starting point for the study. This paper begins with an overview of BT and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between BT and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.
Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data sharing. However, in a majority of existing blockchain-based CP-ABE schemes, private keys were still issued by one central authority that would lead to heavy computation, higher transaction costs, and restricted scalability within the decentralized system. To address these challenges, we present an enhancement approach towards utilizing distributed key management service (KMS) and zero-knowledge paradigms. In our improved novel blockchain system model, we define two types of blockchain nodes for the CP-ABE scheme through staking mechanism. Firstly, the proxy re-encryption nodes are introduced to offer secure multi-party management and distribution of the CP-ABE's master secret key, eliminating dependence on a central authority and producing proofs of re-encryption correctness. Secondly, the operator nodes can collect all transactional information in blockchain-based CP-ABE scheme and then send the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) proofs to verify the batch’s integrity via smart contract. Subsequently, we employ the staking economic incentive model with reward determination and slashing in the decentralized blockchain system to ensure network security. Finally, simulation results validate the effectiveness of our proposed scheme in achieving secure and efficient data sharing. Even amidst the pressure of 100 simultaneous transactions, the average response time for a single node remains at an approximate 28 s. Additionally, there is a notable decrease in on-chain gas consumption, with a gas reduction exceeding 61%. Comparative analyses further indicate that our blockchain-based CP-ABE scheme, in conjunction with a decentralized KMS, offers a superior balance between computational efficiency and functional capability.
Sandro Amofa, Qi Xia, Hu Xia, Isaac Amankona Obiri · 7 authors
Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.
The escalating adoption of Internet of Things (IoT) technologies produces vast amounts of real-time data, which can be shared and analyzed to enhance productivity and product quality across various industries. Unfortunately, the current IoT systems rely heavily on cloud servers for centralized data management. These systems not only suffer from the single point of failure problems but also have data security issues. The malicious users may compromise user privacy or inject corrupted data during the data sharing process. In this article, we propose a secure and efficient data sharing scheme for IoT. First, the scheme integrates blockchain with the distributed database, utilizing smart contracts to ensure secure data storage, querying, and sharing in IoT. Second, we incorporate a reputation mechanism into the data sharing process. This allows IoT users to receive reputation feedback from their partners based on their behaviours, which is dynamically updated as a reputation score on the blockchain by smart contracts. Therefore, honest users get more opportunities to share data, while malicious users are held accountable and revoked from the IoT system. We also harness advanced cryptographic techniques to ensure the submission of reputation feedback does not disclose the user’s private information, preventing vindictive actions from malicious users. Finally, we demonstrate the security and effectiveness of the proposed scheme by conducting the security analysis and the detailed experiments.
Unmanned aerial vehicles (UAVs) empowered Internet of things (IoT) networks have emerged as a burgeoning paradigm in the era of 6G. However, due to substantial data volume and privacy concerns, the conventional UAV backhaul to cloud center framework is not applicable to various latency and privacy-sensitive applications. Therefore, we propose a blockchain-based hierarchical federated learning (FL) framework for UAV-enabled IoT networks. Specifically, we utilize the total data distance-aware device association to mitigate model impairment arising from imbalanced data distribution. Besides, we introduce a lightweight blockchain into FL to tackle the trust deficit caused in decentralized global model aggregation. Furthermore, we design an optimization framework that jointly orchestrating device association, wireless resource allocation, and UAV deployment, aiming at a balance between the learning latency and model accuracy. To address the formulated optimization problem, we proposed a two-stage algorithm that integrates both greedy strategy and soft actor-critic algorithm. Extensive experiments show that our proposed scheme outperforms contemporary relative to state-of-the-art alternatives.
The logistics Internet of Things is a new generation logistics model that integrates advanced network communication technology in the traditional logistics industry. It can achieve intelligent and efficient logistics management and personalized services and has attracted the attention of massive researchers and industrial practitioners. However, the leakage of logistics privacy and chaotic access control mechanisms are still noteworthy issues. In this paper, we propose a privacy-preserving scheme with bidirectional option for blockchain-enhanced logistics internet of things, named PB-LIoT. The scheme is based on the blockchain-enhanced logistics IoT architecture, using smart contracts to achieve data access control, ciphertext-policy attribute-based encryption to achieve privacy, and hash function to achieve data integrity detection. To find more efficient delivery routes, we design a logistics routing selection algorithm based on objective optimization. This algorithm considers time efficiency, transportation cost, workload, and other factors, and uses objective optimization to optimize the path. More importantly, we devise a bidirectional choice strategy to achieve more humane services, not only for customers, but also for express delivery sites. Finally, we analyze the security and performance of the scheme. The results show that the proposed scheme in this paper has data privacy protection and efficiency, while considering the humanized factor of bidirectional option.
Farah Abu-Dabaseh, Mahmoud Alghizzawi, Baker Ibrahim Alkhlaifat, Abd Alrahman Ratib Ezmigna · 7 authors
the emergence of decentralized social systems has provided multiple opportunities and challenges in the areas of privacy and security. Centralized platforms are sensitive to many vulnerabilities, including data breaches, illegal data sharing, and the possibility of censorship. Although decentralized platforms have been developed to address some concerns, there are challenges, especially in the areas of identity management and maintaining secure communication channels. Accordingly, this study aims to explore the possibility of using blockchain technology as a means of improving privacy and security in decentralized social networks. As the use of blockchain technology in the field of identity management, data encryption, and secure peer-to-peer communications, there are potential impacts and difficulties, especially in finding a good mix between openness and transparency. The results of our study show that the use of blockchain technology has a lot of potential to help solve many problems that already exist. However, it is important to be careful and use a well-thought-out plan while implementing it to make the most of its potential benefits. Finally, it discusses potential directions for future research and stresses how important it is to conduct more research in a field that is changing very rapidly.
Federated Learning (FL) allows the collaborative training of a global model in Vehicular Ad-hoc Networks (VANETs): data is maintained on the owner's device and the local gradient updates to the model are aggregated through a secure protocol. However, despite its many advantages, FL is vulnerable to various attacks from malicious clients. Current defenses have several weaknesses. For example, the server may still select malicious clients for aggregation even after they have been identified in previous rounds. They haven't considered the continual monitoring of clients to resist their possible defection or collusion that occurs throughout the FL training process. In response to the weaknesses, we describe a new aggregation model with continuous authentication suits for VANETs. The authentication implementation relies on a non-interactive zero-knowledge proof which preserves privacy. We also minimize the computation and communication overhead by designing a two-phase aggregation scheme, while introducing the Edge Devices (EDs) to assist the FL procedure. Finally, we introduce an application of such a model for VANETs. We describe the prototype implementation and experimentally confirm that the aggregation overhead of the client grows linearly and achieves training speed up over the prior work.
Federated learning allows multiple clients to collaboratively train a global model without revealing their private data. Despite its success in many applications, it remains a challenge to prevent malicious clients to corrupt the global model through uploading incorrect model updates. Hence, one critical issue arises in how to validate the training is truly conducted on legitimate neural networks. To address the issue, we proposeVPNNT, a zero-knowledge proof scheme for neural network backpropagation.VPNNTenables each client to prove to others that the model updates (gradients) are indeed calculated on the global model of the previous round, without leaking any information about the client's private training data. Our proof scheme is generally applicable to any type of neural network. Different from conventional verification schemes constructing neural network operations by gate-level circuits, we improve verification efficiency by formulating the training process using custom gates — matrix operations, and apply an optimized linear time zero knowledge protocol for verification. Thanks to the recursive structure of neural network backward propagation, common custom gates are combined in verification thereby reducing prover and verifier costs over conventional zero knowledge proofs. Experimental results show thatVPNNTis a lightweighted verification scheme for neural network backpropagation with an improved prove time, verification time and proof size.
Blockchain has been recognized as a secure technology with which unique and tamper-proof certificates can be issued. Today the secure and immutable storage of vital records, such as birth certificates, is of paramount importance. Traditional systems for recording and managing such documents are complex, tedious, and inaccessible for many. At present, issuing a certificate involves three stakeholders; Parents, Hospitals, and Registrars. The Parents must visit the Registrar and report the birth after which the Registrar verifies the birth, which is a time-intensive process for the Registrar. To address these challenges, we present a web application that leverages blockchain technology to record and manage birth certificates. By utilizing a decentralized and distributed ledger, our application improves accessibility, transparency, and increased security. This paper outlines the design, implementation, and evaluation of our birth certificate recording system, highlighting its benefits for hospitals, registrars, and individuals. Furthermore, limitations and the future work of this technology are discussed.
Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user’s details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management.
Blockchain is a revolutionary technology for constructing trustworthy communications for 6G multiaccess edge computing (6G-MEC). Designing a blockchain-based access control system for 6G-MEC is challenging due to the highly heterogeneous edge devices (EDs) in 6G-MEC. However, traditional blockchain-based access control methods cannot satisfy the heterogeneous device scenarios and are unable to assign voting weights based on the performance of EDs. In this article, we propose a trustworthy access control method for 6G-MEC networks and demonstrate how blockchain can be utilized in our proposed blockchain-assisted multiaccess control approach. Our approach develops an attribute validation and a validation weight method to offer strengthened access control in a decentralized context. We have proposed a trustworthy access control for multiaccess edge computing by using a multitier blockchain architecture and a reinforcement-learning-based weight determination algorithm for ED to eliminate the influence on blockchain consensus behavior. Experimental results demonstrate the effectiveness of our proposed approach.
Haiqin Wu, Boris Düdder, Shunrong Jiang, Liangmin Wang
Privacy-aware task allocation/matching has been an active research focus in crowdsourcing. However, existing studies focus on an honest-but-curious assumption and a single-attribute matching model. There is a lack of adequate attention paid to scheme designs against malicious behaviors and supporting user-side personalized task matching over multiple attributes. A few recent works employ blockchain and cryptographic techniques to decentralize the matching procedure with verifiable and privacy-preserving on-chain executions. However, they still bear expensive on-chain overhead. In this paper, we propose VP$^{2}$-Match, a blockchain-assisted (publicly) verifiable privacy-aware crowdsourcing task matching scheme with personalization. VP$^{2}$-Match extends symmetric hidden vector encryption for user-side expressive matching without compromising their privacy. It avoids costly on-chain matching by letting the blockchain only store evidence/proofs for public verifiability of the matching correctness and for enforcing fair interactions against misbehaviors. Specifically, we construct extended attribute sets and solve matching verification by an algorithmic reduction into subset verification with an accumulator for proof generation. Formal security proof and extensive comparison experiments on Ethereum demonstrate the provable security and better performance of VP$^{2}$-Match, respectively.
Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest research, Flamingo (S&P 2023), designed a share-transfer-based reusable secret key to support the server continuously performing multiple rounds of aggregation. Nevertheless, the share transfer mechanism it proposed can only be achieved with P probability, which has limited reliability. To tackle the aforementioned problems, we propose a more reliable and anonymously authenticated scheme called Chu-ko-nu for multi-round secure aggregation. Specifically, in terms of share transfer, Chu-ko-nu breaks the probability P barrier by supplementing a redistribution process of secret key components (the sum of all components is the secret key), thus ensuring the reusability of the secret key. Based on this reusable secret key, Chu-ko-nu can efficiently perform consecutive aggregation in the following rounds. Furthermore, considering the client identity authentication and privacy protection issue most approaches ignore, Chu-ko-nu introduces a zero-knowledge proof-based authentication mechanism. It can support clients anonymously participating in FL training and enables the server to authenticate clients effectively in the presence of various attacks. Rigorous security proofs and extensive experiments demonstrated that Chu-ko-nu can provide reliable and anonymously authenticated aggregation for FL with low aggregation costs, at least a 21.02% reduction compared to the state-of-the-art schemes.
Vibha Nehra, Aakarsh MJ, Hitesh Khanna, Naman Jindal
Decentralized digital identity verification systems based on blockchain technology, or D.D.I.V.S as they are popularly known, are linchpins for an entirely new way of authenticating our online identities. These systems possess a number of obvious merits compared to the traditional methods of identity verification, for instance elevated security level, privacy protection and practicability. This article introduces a Prototype system using MetaMask, a very well-known cryptocurrency wallet and Web3 gateway. The system offers significantly superior results than traditional approaches. Its parts include user registration and data extraction by means of public key registration, secure key storage with MetaMask, an uninterrupted connection between D-Apps (Decentralized Applications) and the wallet; and application of asymmetric cryptography to data encryption and decryption. Strict testing has confirmed the MetaMask-powered prototype system can provide a secure, private and practical decentralized digital identification verification platform. That heralds a promising change for all of us