2024

A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Yuming Tang#, Yitian Zhang#, Tao Niu, Zhen Li*, Zijian Zhang, Huaping Chen, Long Zhang (# equal contribution, * corresponding author)

Computer Modeling in Engineering and Sciences 2024

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.

[Paper]

A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis
A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis

Yuming Tang#, Yitian Zhang#, Tao Niu, Zhen Li*, Zijian Zhang, Huaping Chen, Long Zhang (# equal contribution, * corresponding author)

Computer Modeling in Engineering and Sciences 2024

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.

[Paper]

2023

Achieving Anonymous and Covert Reporting on Public Blockchain Networks

Liehuang Zhu, Jiaqi Zhang, Can Zhang, Feng Gao, Zhuo Chen, Zhen Li* (* corresponding author)

Mathematics 2023

Reporting helps to combat illegal activities and deters lawbreakers and potential lawbreakers. From ancient times to the present, public authorities have usually rewarded effective reporting information to build harmonious societies. In this process, protecting the privacy of the whistleblower is a very important issue. Existing blockchain-based anonymous reporting solutions help solve the problem of insufficient anonymity in traditional reporting solutions, but they do not address the issue of hiding the reporting behavior. The disclosure of reporting behavior may alert offenders in advance and negatively impact case handling. This paper proposes an anonymous and covert reporting scheme and rewarding mechanism based on blockchain, which realizes the covertness of the reporting behavior while protecting the privacy of the whistleblower. The proposed scheme uses ring signature and derived address technology to ensure anonymity and achieves covertness by embedding information in the ring signature based on the idea of covert communication. Theoretical analysis proves that the proposed scheme has covertness, anonymity, and unforgeability properties. Experiments show that the proposed scheme takes only 0.08 s to upload data and 0.07 s to verify while achieving covertness.

[Paper]

Achieving Anonymous and Covert Reporting on Public Blockchain Networks
Achieving Anonymous and Covert Reporting on Public Blockchain Networks

Liehuang Zhu, Jiaqi Zhang, Can Zhang, Feng Gao, Zhuo Chen, Zhen Li* (* corresponding author)

Mathematics 2023

Reporting helps to combat illegal activities and deters lawbreakers and potential lawbreakers. From ancient times to the present, public authorities have usually rewarded effective reporting information to build harmonious societies. In this process, protecting the privacy of the whistleblower is a very important issue. Existing blockchain-based anonymous reporting solutions help solve the problem of insufficient anonymity in traditional reporting solutions, but they do not address the issue of hiding the reporting behavior. The disclosure of reporting behavior may alert offenders in advance and negatively impact case handling. This paper proposes an anonymous and covert reporting scheme and rewarding mechanism based on blockchain, which realizes the covertness of the reporting behavior while protecting the privacy of the whistleblower. The proposed scheme uses ring signature and derived address technology to ensure anonymity and achieves covertness by embedding information in the ring signature based on the idea of covert communication. Theoretical analysis proves that the proposed scheme has covertness, anonymity, and unforgeability properties. Experiments show that the proposed scheme takes only 0.08 s to upload data and 0.07 s to verify while achieving covertness.

[Paper]

Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Yitian Zhang#, Yuming Tang#, Zijian Zhang, Meng Li, Zhen Li*, Salabat Khan, Huaping Chen, Guoqiang Cheng (# equal contribution, * corresponding author)

Mathematics 2023

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

[Paper]

Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness
Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Yitian Zhang#, Yuming Tang#, Zijian Zhang, Meng Li, Zhen Li*, Salabat Khan, Huaping Chen, Guoqiang Cheng (# equal contribution, * corresponding author)

Mathematics 2023

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.

[Paper]

A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain

Zijian Zhang, Shuqi Wang, Zhen Li*, Feng Gao, Huaqiang Wang (* corresponding author)

Mathematics 2023

Covert communication was widely studied in recent years in terms of keeping the communication of entities on the Internet secret from the point of view of information security. Due to the anonymity of accounts and the publicness of the ledger, blockchain is a natural and ideal channel for helping users establish covert communication channels. Senders can embed secret messages into certain fields in transactions, and receivers can extract those messages from the transactions without attracting the attention of other users. However, to the best of our knowledge, most existing works have aimed at designing blockchain-based covert communication schemes. Few studies concentrated on the recognition of transactions used for covert communication. In this paper, we first analyze convolutional neural network (CNN)-based and attention-based covert transaction recognition schemes, and we explore the deep relationship between the appropriate extraction of features and the embedded fields of covert transactions. We further propose a multi-dimensional covert transaction recognition (M-CTR) scheme. It can simultaneously support both one-dimensional and two-dimensional feature extraction to recognize covert transactions. The experimental results show that the precision and recall of the M-CTR in recognizing covert transactions outperformed those of existing covert communication schemes.

[Paper]

A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain
A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain

Zijian Zhang, Shuqi Wang, Zhen Li*, Feng Gao, Huaqiang Wang (* corresponding author)

Mathematics 2023

Covert communication was widely studied in recent years in terms of keeping the communication of entities on the Internet secret from the point of view of information security. Due to the anonymity of accounts and the publicness of the ledger, blockchain is a natural and ideal channel for helping users establish covert communication channels. Senders can embed secret messages into certain fields in transactions, and receivers can extract those messages from the transactions without attracting the attention of other users. However, to the best of our knowledge, most existing works have aimed at designing blockchain-based covert communication schemes. Few studies concentrated on the recognition of transactions used for covert communication. In this paper, we first analyze convolutional neural network (CNN)-based and attention-based covert transaction recognition schemes, and we explore the deep relationship between the appropriate extraction of features and the embedded fields of covert transactions. We further propose a multi-dimensional covert transaction recognition (M-CTR) scheme. It can simultaneously support both one-dimensional and two-dimensional feature extraction to recognize covert transactions. The experimental results show that the precision and recall of the M-CTR in recognizing covert transactions outperformed those of existing covert communication schemes.

[Paper]

Privacy Preservation Authentication: Group Secret Handshake with Multiple Groups

Dong Han, Zhen Li, Mengyu Wang*, Chang Xu*, Kashif Sharif (* corresponding author)

Mathematics 2023

The technique of group secret handshake (GSH) has been used to help the members affiliated with the same group in achieving private authentication. After executing GSH protocols, the participants affiliated with the group can compute a shared secret key, or generate a public encryption key while the true participants can self-compute their decryption keys. This paper presents a concrete GSH protocol with Multiple Groups. Only a legitimate member can prove that it belongs to a set of legitimate affiliations, but which affiliation it belongs to will not be leaked. The Group Authority can reveal the real identities of the fellows in the proposed scheme after analyzing the flow of communication. The proposed scheme can provide affiliation-hiding and detectability. In addition, it achieves Perfect Forward Secrecy.

[Paper]

Privacy Preservation Authentication: Group Secret Handshake with Multiple Groups
Privacy Preservation Authentication: Group Secret Handshake with Multiple Groups

Dong Han, Zhen Li, Mengyu Wang*, Chang Xu*, Kashif Sharif (* corresponding author)

Mathematics 2023

The technique of group secret handshake (GSH) has been used to help the members affiliated with the same group in achieving private authentication. After executing GSH protocols, the participants affiliated with the group can compute a shared secret key, or generate a public encryption key while the true participants can self-compute their decryption keys. This paper presents a concrete GSH protocol with Multiple Groups. Only a legitimate member can prove that it belongs to a set of legitimate affiliations, but which affiliation it belongs to will not be leaked. The Group Authority can reveal the real identities of the fellows in the proposed scheme after analyzing the flow of communication. The proposed scheme can provide affiliation-hiding and detectability. In addition, it achieves Perfect Forward Secrecy.

[Paper]