Muhammed Siraj, Mohd Izuan Hafez Ninggal, Nur Izura Udzir, Muhammad Daniel Hafiz Abdullah · 5 authors
Sensitive records stored in the cloud such as healthcare records, private conversation and credit card information are targets of hackers and privacy abuse. Current information and record management systems have difficulties achieving privacy protection of such sensitive records in a secure, transparent, decentralized and trustless environment. The Blockchain technology is a nascent and a promising technology that facilitates data sharing and access in a secure, decentralized and trustless environment. The technology enables the use of smart contracts that can be leveraged to complement existing traditional systems to achieve security objectives that were never possible before. In this paper, we propose a framework based on Blockchain technology to enable privacy-preservation in a secured, decentralized, transparent and trustless environment. We name our framework SmartCoAuth. It is based on Ethereum Smart Contract functions as the secure, decentralized, transparent authentication and authorization mechanism in the framework. It also enables tamper-proof auditing of access to the protected records. We analysed how SmartCoAuth could be integrated into a cloud application to provide reliable privacy-preservation among stakeholders of healthcare records stored in the cloud. The proposed framework provides a satisfactory level of data utility and privacy preservation.
Yang Liu, Debiao He, Min Luo, Huaqun Wang · 5 authors
With the number of smart vehicle drivers increasing rapidly, privacy-preserving identity management methods in Vehicular Ad-hoc Networks (VANETs) become more imperative and receive much attention from researchers. Since identity leakage or a single point of failure may result in serious consequences in the VANET, the decentralized anonymous credential (DAC) could be a potential approach to construct a robust network. However, the supervision under decentrilized environment could be a necessary and troublesome in the VANETs. In this paper, we present an anonymous traceable and revocable credential system using blockchain, called ATRC, built over a generalized group signature. The underlying group signature not only meets the anonymity requirement but also makes the users master their identities. What's more, to tackle the privacy leakage in the revocation process, we employ the Merkle tree to construct a whitelist, which trades off the efficiency and the privacy. Finally, we gives the comparison and experiment performance to show our scheme holds lightweight on the user side and has fewer computation costs in the show and revocation phase.
Duo Zhang, Shangping Wang, Qian Zhang, Yaling Zhang
Cloud computing has brought great convenience to data storage and resource sharing, however, there are still concerns about data security and service quality. Attribute-based encryption (ABE) and searchable encryption (SE) are always adopted to achieve data access authorization and data retrieval on encrypted data in data sharing, respectively. But more efficient and accurate methods have been always pursued. Moreover, the spoofing attack is another important aspect that raises concerns, especially when it comes to reliability of search results and online payments. Blockchain, an emerging technology that can be used to solve the problem of trust, has shown great application potential in finance and data sharing. Based on blockchain, we propose an attribute-based conjunctive keyword search (ABCKS) scheme with verifiability and fairness. In this paper, data privacy-preserving, fine-grained access control, and multi-keywords search can be supported simultaneously. Blockchain and smart contract are employed to facilitate the search result verification process and ensure fair payment in the trustless case. Furthermore, we reduce the user's decryption load to a constant level by performing partial decryption on the cloud side. Finally, the results of the performance evaluation indicate that our scheme has higher efficiency and security.
Qianlong Wang, Weixian Liao, Yifan Guo, Michael P. McGuire · 5 authors
With the proliferation of computationally powerful edge devices, edge computing has been widely adopted for wide-ranging computational tasks. Among these, edge artificial intelligence (AI) has become a new trend, allowing local devices to work cooperatively and build deep learning models. Federated learning is one of the representative frameworks in distributed machine learning paradigms. However, there are several major concerns with existing federated learning paradigms. Existing distributed frameworks rely on a central server to coordinate the computing process, where such a central node may raise security concerns. Federated learning also relies on several assumptions/requirements, e.g., the independent and identically distributed (i.i.d.) data and model homogeneity. Since more and more edge devices are able to train lightweight models with local data, such models are normally heterogeneous. To tackle these challenges, in this article, we develop a blockchain-empowered federated learning framework that enables learning in a fully decentralized manner while taking the model heterogeneity and data heterogeneity into account. In particular, a federated learning framework with a heterogeneous calibration process, i.e., Model and Feature Calibration (FL-MFC), is developed to enable collaboration among heterogeneous models. We further design a two-level mining process using blockchain to enable the secure decentralized learning process. Experimental results show that our proposed system achieves effective learning performance under a fully heterogeneous environment.
The rapid development of Internet of Things (IoT) enables digital twin (DT) technology to precisely represent a real product in a virtual space by generating a multitude of IoT data items to record many aspects of the product. To support various DT-based applications, the generated IoT data items need to be shared among multiple parties involving the lifecycle of the product, which raises increasing demand for data access control. The decentralization and tamper-proofing properties of blockchain enable it a promising technology to support immutability protection of shared IoT data items. Meanwhile, to protect the confidentiality of the shared IoT data items, attribute-based encryption (ABE) can be used as a common tool to construct a cryptographic enforced data access control scheme. However, its adoption has been severely hindered by the incompatibility between the immutability of blockchain and secure authority update of cryptographic enforced data access control. In this paper, a blockchain-aware rollbackable data access control scheme (Bdacs) is proposed to reconcile the above tension. Bdacs uses two novel encryption schemes named hierarchical encryption scheme and privacy-preserving rollback re-encryption scheme to realize secure dynamic access control while preserving the immutability of blockchain. We prove the security of Bdacs and evaluate it through theoretical comparison and experimental analysis to confirm its efficiency. This work can serve as a basis of development of future DT-based applications to enable privacy-preserving IoT data-sharing systems deployed on blockchain.
Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.
Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.
Biegon Kipkoech Collins, Alex Kibet, Andrew M. Kahonge
Amidst rapid technological advancement and digital transformation, ensuring privacy and data security is paramount. Governments and digital service providers face the challenge of establishing secure identification systems that protect individuals' personal information while enabling reliable authentication and seamless user experiences. Traditional identification methods often require individuals to disclose sensitive personal information, leading to privacy risks and potential data breaches. Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address these concerns. By leveraging ZKPs, individuals can authenticate their identities or assert specific attributes without revealing sensitive data. This approach holds great potential for preserving privacy while enabling efficient and trustworthy verification processes. This paper explored ZKPs and how governments and digital service providers can utilize this technology to achieve secure identification while upholding privacy. A key focus was prototyping a secure identification protocol using ZKPs. Through practical implementation, this research aimed to demonstrate the reliability and effectiveness of ZKPs in real-world scenarios. Keywords : zero-knowledge proofs, privacy, digital identity, governments, digital services. DOI: 10.7176/ISDE/13-2-06 Publication date: September 30 th 2023
Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.
Digital twin (DT) constructs virtual counterparts of physical devices to monitor and optimize their life cycle processes. With the emergence of industry 4.0, Industrial Internet of Things (IIoT) has became the backbone of the DT by providing a fundamental way to transform physical devices to their virtual counterparts. With the deployment of IIoT, built-in sensors enable real-time collection of critical DT data involving various physical parameters associated with devices during their life cycle. However, traditional data sharing services rely on a centralized infrastructure, which inevitably brings severe security threats to share large volume of sensitive DT data derived from numerous sensors. To address the above issue, this paper presents a blockchain based Multi-users Oblivious Data Sharing scheme (MODS) for the digital twin system in the context of IIoT. MODS supports a broad range of security properties including confidentiality, obliviousness, and access control for the DT data stored on the blockchain. MODS adopts a hybrid design approach by combing trusted hardware and cryptography to achieve well balances between security and efficiency. To demonstrate the design advantages of MODS, we explore the design space of a multi-users oblivious data sharing scheme by using pure cryptographic approach, which incurs several design tradeoffs that must be addressed. We show that MODS performs well in these tradeoffs. A comprehensive evaluation has been conducted to demonstrate that MODS is practical to support secure data sharing via blockchain for IIoT.
Moayad Aloqaily, Ismaeel Al Ridhawi, Salil S. Kanhere
The Internet of Things (IoT) has revolutionized the manufacturing process in the industry. It has created a new ecosystem allowing a diversified set of devices to be controlled remotely with minimal human intervention. Today, with the advances in intelligence, processing, storage, communication, and networking capabilities of IoT devices, we are one step closer to realizing the vision of Industry 4.0. Cyber-physical systems (CPS) are now significantly more intelligent and automated with the aid of advances in Machine Learning (ML). Intelligent IoT (IIoT), Digital Twins (DT) and the advances in mobile networks are now paving the path towards decentralized self-managed CPS in the industry. DT permits mobile networks to provide adaptive and dynamic configurations for cooperative CPS. Moreover, trustworthy cooperation may be realized with blockchain. In this article, we present a blockchain-assisted hierarchical federated learning (FL)-enabled platform (HFL) for Industry 4.0. The solution integrates DT into CPS to accurately capture the characteristics of industrial IoT devices and assist in the HFL process. A two-stage FL algorithm is used that groups Internet-enabled factory machinery and their DTs into groups in accordance with their organizational structure. A global model is created for the groups from the averaged local models and the DT model in the first stage. During the second stage, federated aggregation is used to create a global model from the first-stage models. Blockchain is used to cross-verify and validate newly added blocks with the support of validator nodes. Numerical analysis is performed to compare between the presented DT-enabled and blockchain-assisted HFL solution and benchmark solutions in terms of network overhead, block optimization, and accuracy.
With the advent of the digital age, traditional lifestyle activities, such as reading books, referencing recipes, and enjoying music, have progressively transitioned from offline to online. However, numerous issues plague the conventional approach to digital copyright protection. This is especially true in the realm of recipe protection, where the rights and interests of original creators are inadequately safeguarded due to the widespread dissemination of a large number of recipes on the Internet. This primarily stems from the high costs of gathering evidence, incomplete coverage of evidence collection, and the inability to identify and halt infringement activities in a timely manner during the process of traditional digital copyright protection. Therefore, this study designs and implements a blockchain-based digital recipe copyright protection scheme to address the issues of insufficient legal evidence and cumbersome processes in traditional digital copyright protection. First, we enhance standard short text similarity calculation method SimHash, boosting the accuracy of text similarity detection. We then utilize the decentralization, immutability, time-stamping, traceability, and smart contract features of blockchain technology for data privacy protection. We employ the Interplanetary File System (IPFS) to store raw data, thereby ensuring user privacy and security. Lastly, we improve the proxy voting node selection in the existing delegated proof of stake (DPOS) consensus mechanism. According thorough evaluation and empirical analysis, the scheme effectively improves the accuracy of text similarity detection. Simultaneously, the enhanced DPOS mechanism effectively rewards nodes with excellent performance and penalizes nodes exhibiting malicious behavior. In this study, we successfully designed and implemented an innovative digital recipe copyright protection scheme. This scheme effectively enhances the accuracy of text similarity detection; ensures the privacy and security of user data; and, through an enhanced DPOS mechanism, rewards well-performing nodes while penalizing those exhibiting malicious behavior.
Summary Recently, blockchain utilization with advanced encryption and decryption has gained improved attention over diverse applications. Especially in the medical field, big data storage requires an effective medium for managing user data with high confidentiality. The existing research presents diverse encryption algorithms, but certain drawbacks include sensitive information leakage, ineffective data integrity verification, and increased computational cost and time. The proposed work develops an effective blockchain mechanism for secured data storage to conquer these limitations. This article secures the medical research data from different team members for intellectual share estimation. Initially, the registration process involves user details such as name, contact number, age and so on. Next, a digital signature is generated for every registered user using the SHA‐256 hash algorithm. During data uploading, the data is encrypted using elliptic curve Diffie Hellman encryption scheme to promote security. The audio and video files are stored in inter planetary file system, and texts are stored in the blockchain. The users can retrieve the stored data from the blockchain using the encryption key. The identities and associated public keys are verified using a smart contract to validate the user certificate. Next, a decision form is made to show the polling mechanism using item preference ranking for other users viewing the data. The proposed mechanism ensures intellectual shares for all the users associated with a specified project. The performances are analyzed using PYTHON, and the encryption time obtained in the proposed model is 0.85 s at 140 security bit level.
The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.
Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.
Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analysed. We propose a Private Token Transfer System (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modelled as a network flow problem. It is shown that in case some balance ranges are deliberately leaked out to particular organizations or adversial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.
Open access
3 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.
Federated learning (FL) enables clients to participate in machine learning tasks in a private way. Applying blockchain into FL for decentralization and security has attracted much attention recently. The blockchain with a directed acyclic graph (DAG) structure enables mobile devices to participate in decentralized FL more flexibly while reducing resource consumption and is more suitable for implementing decentralized FL in mobile networks than traditional blockchains. Non-independent and identically distributed (non-IID) data is a common problem in FL. Existing work on DAG-based FL lacks a suitable optimization method for non-IID data. In this paper, we briefly describe a DAG-based FL approach in mobile networks. In order to mitigate the negative effects of non-IID data, consensus in DAG is improved by utilizing a new tip (Unconfirmed blocks in the DAG ledger) selection algorithm proposed in this paper to help clients find suitable models more easily in DAG-based FL. Experiments on multiple datasets show that the method proposed in this paper has better results than existing work and is closer to traditional FL.
E. Suresh Babu, Mekala Srinivasa Rao, Gandharba Swain, A. Kousar Nikhath · 5 authors
Abstract The technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss.
Aitizaz Ali, Bander Ali Saleh Al‐rimy, Ting Tin Tin, Saad Altamimi · 6 authors
Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain's immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.
With the increasing presence of blockchain-based distributed applications in various aspects of daily life, there has been a growing focus on the privacy protection of blockchain ledgers and the corresponding regulatory technologies. However, current mainstream solutions primarily concentrate on the verifiable encryption of blockchain transaction addresses and contents, neglecting the regulatory requirements for private transactions. Moreover, the few monitorable solutions suffer from issues such as excessive centralization and a single-minded approach to regulatory content. To address these deficiencies, this paper proposes a blockchain privacy-preserving scheme that supports multi-level regulation through the utilization of zero-knowledge proofs (zk-SNARKs) and attribute-based encryption (ABE). Firstly, by leveraging zk-SNARKs, this scheme achieves blockchain privacy-preserving within an account model, enabling the concealment of user transaction addresses and values. Secondly, by employing attribute-based encryption, a multi-level regulatory model is developed alongside the privacy protection measures, allowing for selective disclosure of transaction content. Finally, we analyze the security of the proposed scheme and compare it with other schemes, discussing its advantages in terms of privacy, security, and regulatory capabilities, we also provide a preliminary evaluation of the scheme's efficiency through experiments. In conclusion, the scheme demonstrates strong privacy by relying on mathematical proofs through zk-SNARKs to ensure security while comprehensively safeguarding content. It also achieves multi-level regulation on the foundation of privacy protection, with comprehensive regulatory coverage and decentralized regulatory authority.
Federated learning (FL) aims to collaboratively train a global model while ensuring client data privacy. However, FL faces challenges from the non-IID data distribution among clients. Clustered FL (CFL) has emerged as a promising solution, but most existing CFL frameworks adopt synchronous frameworks lacking asynchrony. An asynchronous CFL framework called SDAGFL based on directed acyclic graph distributed ledger techniques (DAG-DLT) was proposed, but its complete decentralization leads to high communication and storage costs. We propose DAG-ACFL, an asynchronous clustered FL framework based on directed acyclic graph distributed ledger techniques (DAG-DLT). We first detail the components of DAG-ACFL. A tip selection algorithm based on the cosine similarity of model parameters is then designed to aggregate models from clients with similar distributions. An adaptive tip selection algorithm leveraging change-point detection dynamically determines the number of selected tips. We evaluate the clustering and training performance of DAG-ACFL on multiple datasets and analyze its communication and storage costs. Experiments show the superiority of DAG-ACFL in asynchronous clustered FL. By combining DAG-DLT with clustered FL, DAG-ACFL realizes robust, decentralized and private model training with efficient performance.