Farhana Javed, Josep Mangues‐Bafalluy, Engin Zeydan, Luis Blanco
This paper proposes a blockchain-enabled framework to enhance trust, transparency, and collaboration in Open Radio Access Network (O-RAN) infrastructures through Federated Learning (FL). Traditional O-RAN architectures and centralized machine learning approaches face challenges when integrating multi-vendor environments, primarily due to lack of trust, proprietary data concerns, and limited interoperability. Our solution transitions from implicit trust, where the reliability of contributions is assumed, to explicit trust, where reputation is verifiably established on-chain. We introduce a blockchain-based reputation mechanism that evaluates the accuracy, integrity, and quality of participants’ model updates within the FL process. Smart contracts automate critical tasks-such as participant registration, model update verification, and reputation scoring-ensuring that data inputs directly influence accountability in a tamper-proof, transparent manner. By deploying the framework on a scalable Layer 2 blockchain (Polygon) testnet and proposing the use of a blockchain oracle within this architectural framework for secure off-chain computations, this work focuses on a conceptual architectural approach by aligning with O-RAN’s architecture to propose and deploy a Decentralized Application (DApp) on the blockchain. The proposed framework emphasizes a conceptual design over performance optimization and is structured to naturally benefit from ongoing improvements in blockchain scalability, which may reduce latency and enhance operational efficiency over time. Smart contracts for crucial processes and reputation calculation are included within our proposed DApp. The implementation of this work is publicly accessiblehttps://github.com/farhanajaved/Reputation_O-RAN.
Blockchain technology has produced effective solutions and provides security by using cryptographic tools for various applications, attracting attention from the academic community. Therefore, researchers have taken advantage of the features of blockchain technology to increase the security of the ecosystem. Recently, as the existence of quantum computers has been felt, researchers have started to benefit from post-quantum cryptography to increase privacy and security. There has been an increase in data and asset protection in post-quantum blockchain-based solutions. To the best of our knowledge, there is no comprehensive review or taxonomy that provides a complete picture of post-quantum secure structures with privacy-preserving techniques that have the potential to be used in blockchain. This paper aims to close this gap by systematically examining these approaches and revealing the deficiencies in the existing literature and the development potential in these areas. The taxonomy examines the role of blockchain technology in post-quantum cryptography and emphasizes the potential of technologies such as zero-knowledge proof to ensure privacy in post-quantum blockchain-based systems. We also review the existing literature on addressing the performance overhead, interoperability, scalability, and security challenges in implementing post-quantum cryptography in zero-knowledge proof-enabled blockchain architectures that protect against quantum computing threats. The studies are collected from journal papers in widely used academic databases between 2018 and 2024. The studies are subjected to certain elimination criteria, and 13 studies are reviewed in detail. Our approach will facilitate discussions on future research directions by proposing the accessibility of post-quantum cryptography against quantum threats to blockchain systems and solutions to the challenges that arise in the integration phase.
In today's data-driven world, vast amounts of information power Machine Learning (ML) models for a wide range of applications. However, this data flow raises significant privacy concerns, as individuals are often reluctant to share personal information, especially given increasing regulations on data protection. Federated Learning (FL) offers a solution by training ML models directly on users' devices and sending only model updates to a central server. This distributed approach enables collaboration without sharing personal data, but challenges remain. Centralization may lead to server bottlenecks, reduced resilience, and fairness concerns if updates from certain devices are prioritized. Additionally, the lack of transparency and accountability can erode trust, while security risks, such as data poisoning and model inversion attacks, further complicate FL. Deployment can be costly and time-consuming, and participants may also lack incentives. Regulatory compliance, such as ensuring the right to be forgotten, adds complexity, as removing data from FL models without full retraining is challenging. This dissertation proposes integrating Distributed Ledger Technologies (DLTs) with FL to address these challenges. DLT decentralizes the aggregation process, enhancing security, transparency, and fairness through immutable record-keeping and traceability. Two DLT-based architectures are presented: one blockchain-based and the other using a Directed Acyclic Graph (DAG) for scalability. These approaches utilize Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to track contributions and verify participants. Furthermore, a DLT-based FL as a Service (FLaaS) is introduced to simplify deployment, incorporating model validation to mitigate poisoning attacks and token-based incentives to encourage participation. Additionally, this dissertation outlines design guidelines for Federated Unlearning (FU), covering key evaluation metrics, existing techniques, and future research. Finally, a new unlearning algorithm is proposed to address adversarial settings and protect model integrity. These contributions pave the way for more secure, transparent, and resilient FL systems that can meet the needs of next-generation data-driven applications.
Jan 1, 2025·Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I
G. Sowmya Bala, P. S. G. Aruna Sri, Satyanarayana Korada, Suneel Gone
Traditional ticketing systems are at risk of fraud, counterfeiting, and issues concerning scalability. In this research, we investigate the application of blockchain technology towards the revolutionary concept of event tickets. We analyze how fundamental attributes of blockchain technology, such as
Dan Bogdanov, Eduardo Brito, Annika Jaakson, Peeter Laud · 5 authors
Abstract This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design and evaluation of a Zero-Knowledge Proof-of-Location (ZK-PoL) system that ensures a vehicle’s adherence to territorial driving requirements without disclosing specific location data, hence maintaining user privacy. Our findings suggest a promising approach to apply ZK-PoL protocols in large-scale governmental subsidy or taxation programs.
The utilization of Zero-Knowledge Proofs (ZKPs) in blockchain technology enhances privacy while simultaneously preserving transparency. Given that blockchain networks frequently elicit privacy concerns owing to the inherently public nature of transaction data, ZKPs present a viable solution by enabling parties to authenticate transactions without disclosing sensitive information. This study primarily concentrates on zk-SNARKs and zk-STARKs, which represent advanced iterations of ZKPs that enhance both privacy and scalability. By analyzing established blockchain protocols, such as Zcash and Ethereum, this research illustrates that ZKPs can effectively safeguard privacy while also facilitating scalability through mechanisms such as zkrollups, which consolidate multiple transactions into a single proof, thereby alleviating congestion on the blockchain. Additionally, ZKPs enhance the verification efficiency, thereby reducing the computational burden on blockchain networks and promoting expedited transactions. However, challenges such as computational overheads and regulatory hurdles persist, hindering the widespread implementation of ZKPs. Future research endeavors should focus on overcoming these challenges by developing more efficient algorithms and collaborating with regulatory authorities to establish clear guidelines for ZKP-based systems. The potential implications of ZKPs extend beyond blockchain technology, offering substantial advantages to sectors such as finance, healthcare, and identity management, in which secure and confidential transactions are paramount. In summary, although ZKPs possess the capacity to transform privacy within decentralized networks, further advancements are required to fully harness their potential and ensure their extensive adoption.
ABSTRACT This manuscript presents an exhaustive review of blockchain‐based mixing services, aiming to fill the existing gap between academic innovations and real‐world implementations. Starting with an identification of the core functionalities and techniques employed by mixing services, the paper delves into detailed explanations of these operational mechanisms. It further outlines an evaluation framework tailored for a rigorous assessment, highlighting the key vulnerabilities and strengths of various solutions. In addition, the study identifies potential attack vectors that compromise these services. The paper explores the dual nature of mixing services: while they contribute to the preservation of privacy—a cornerstone of blockchain technologies—they can also facilitate illicit activities. By addressing key research questions, this study not only offers a comprehensive overview of the current state of mixing services but also sets the stage for future academic discourse in this evolving field.
Blockchain technology has become a game-changer in strengthening data security and transparency within decentralized systems. This study examines its role in tackling major challenges related to data integrity, access control, and auditability, with a focus on industries like finance, decentralized finance (DeFi), and supply chain management. Using data analysis, case studies, and expert opinions, the research highlights how blockchain provides a secure, transparent, and efficient framework for digital transactions. The findings reveal that blockchain enhances security by eliminating weaknesses found in traditional centralized systems. With features like immutable record-keeping, cryptographic encryption, and decentralized control, blockchain ensures transactions remain secure, tamper-proof, and resilient against fraud and cyber threats. Additionally, its ability to maintain a shared, verifiable ledger fosters transparency and builds trust among stakeholders. Smart contracts further improve efficiency by automating processes and ensuring compliance with predefined rules. However, despite its many benefits, blockchain adoption faces hurdles such as scalability challenges, regulatory uncertainties, and integration difficulties. To overcome these barriers, the study suggests developing clear regulatory guidelines, implementing advanced scalability solutions, increasing awareness and technical training, promoting cross-industry collaboration, and adopting hybrid blockchain models that balance security with privacy. In conclusion, this research emphasizes blockchain’s potential to reshape data security and transparency across various sectors. By addressing existing challenges, blockchain can drive innovation, streamline operations, and enhance trust in digital ecosystems.
Burhan Ul Islam Khan, Khang Wen Goh, Abdul Raouf Khan, Megat F. Zuhairi · 5 authors
A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users’ quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system.
Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training data remains on user devices. In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security has become a critical obligation. ML-based service providers are not only confronted with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as the General Data Protection Regulation (GDPR) Federated Learning is very important to reduce data privacy risks. Federated Learning is a scheme in which several consumers work collectively to unravel machine learning problems, with a dominant collector synchronizing the procedure. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine-learning perspective. This paper investigates the potential of Federated Learning for privacy-preserving machine learning in domains like healthcare, finance and IOT, where data privacy is paramount. We explore existing techniques to enhance privacy, including differential privacy, secure aggregation, homomorphic encryption, federated learning with encrypted, meta-learning, machine learning, privacy-preserving techniques, blockchain technology, decentralized learning, federated averaging, data privacy, searchable encryption and zero-knowledge proofs. This paper concludes with future research directions to address ongoing challenges & further enhance the effectiveness & scalability of privacy-preserving federated learning.
Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. However, traditional FL methods face challenges in ensuring trust, security, and efficiency, particularly in heterogeneous environments with varying computational capacities. To address these issues, we propose a blockchain-based trusted federated learning method that integrates FL with consortium blockchain technology. This method leverages computational power registration to group participants with similar resources into private chains and employs cross-chain communication with a central management chain to ensure efficient and secure model aggregation. Our approach enhances communication efficiency by optimizing the model update process across chains, and it improves security through blockchain’s inherent transparency and immutability. The use of smart contracts for participant verification, model updates, and auditing further strengthens the trustworthiness of the system. Experimental results show significant improvements in communication efficiency, model convergence speed, and security compared to traditional federated learning methods. This blockchain-based solution provides a robust framework for creating secure, efficient, and scalable federated learning environments, ensuring reliable data sharing and trustworthy model training.
The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things (IoT) devices. This article addresses the privacy and security issues brought up by data sharing in the context of IoT fog computing. The suggested framework, called “BlocFogSec”, secures key management and data sharing through blockchain consensus and smart contracts. Unlike existing solutions, BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing, while employing a consensus protocol to validate transactions and maintain blockchain integrity. To process and store data effectively at the network edge, the framework makes use of fog computing, notably reducing latency and raising throughput. BlocFogSec successfully blocks unauthorized access and data breaches by restricting transactions to authorized nodes. In addition, the framework uses a consensus protocol to validate and add transactions to the blockchain, guaranteeing data accuracy and immutability. To compare BlocFogSec's performance to that of other models, a number of simulations are conducted. The simulation results indicate that BlocFogSec consistently outperforms existing models, such as Security Services for Fog Computing (SSFC) and Blockchain-based Key Management Scheme (BKMS), in terms of throughput (up to 5135 bytes per second), latency (as low as 7 ms), and resource utilization (70% to 92%). The evaluation also takes into account attack defending accuracy (up to 100%), precision (up to 100%), and recall (up to 99.6%), demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.
Xin Wang, Li Jiaqian, Ding Xueshuang, H. Zhang · 5 authors
The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. Differential privacy(DP) technology is applied in federated learning(FL). By adding noise to raw data and model parameters, it can further enhance the degree of data privacy protection. Over the years, differential privacy technology based on federated learning framework has been developed, which is divided into central differential privacy federated learning(CDPFL) and local differential privacy federated learning(LDPFL). Although differential privacy may reduce the accuracy and convergence of federated learning models while protecting data privacy, researchers have proposed a variety of optimization methods to balance privacy protection and model performance. This paper comprehensively expounds the research status of differential privacy techniques based on the federated learning framework, first providing detailed introductions to federated learning and differential privacy technologies, and then summarizing the development status of two types of federated learning differential privacy(DPFL) techniques respectively; for CDPFL, the paper divides the discussion into first proposal of CDP and typical application examples, the impact of Gaussian mechanisms on model accuracy, optimization based on asynchronous differential privacy, and insights from other scholars; for LDPFL, the paper divides the discussion into first proposal of LDP and typical application examples, processing multidimensional data and improving model accuracy, existing methods and optimization for reducing communication costs, balancing privacy protection and data usability, LDPFL based on the Shuffle model, and insights from other scholars; following this, the paper addresses and summarizes the unique challenges introduced by incorporating differential privacy into federated learning and proposes solutions; finally, based on a summary of existing optimization techniques, the paper outlines future directions and specifically discusses three research ideas for enhancing the optimization effects of federated differential privacy: advanced optimization strategies combining Bayesian methods and the Alternating Direction Method of Multipliers (ADMM), integrating lattice homomorphic encryption techniques from cryptography to achieve more efficient differential privacy protection in federated learning, and exploring the application of zero-knowledge proof techniques in federated learning for privacy protection.
Huda A. Ahmed, Hend Muslim Jasim, Ali Noori Gatea, Ali Amjed Ali Al-Asadi · 5 authors
Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches.
Identification, authentication, and authorization processes can be conducted in various ways. Particular attention is given to the processes implemented within the self-sovereign identity paradigm. This paper analyses the processes from a data leak perspective. A comparison is made between self-sovereign identity and a centralized identity provider scheme. An overview of the relevant implementations for these processes is provided: in both the self-sovereign and non-sovereign paradigms. It has been found that, from the data leaks perspective, the self-sovereign identity scheme could only provide superior security if zero-knowledge proof technology is applied.
The massive data generated by the Internet of Things (IoT) is often outsourced to the cloud, leading to a separation between data ownership and management. Access control during the data’s validity period and assured deletion once that period expires are both crucial for protecting privacy. While recent research has primarily focused on access control, assured deletion has received less attention. Existing assured deletion schemes can be classified into key-control based and cryptographic policy based methods, but to varying degrees, they have limitations such as requiring a trusted third party, high encryption overhead, lack of support for deletion verification and fine-grained access control. To address these limitations, we propose BBAD, a blockchain-based assured deletion scheme that leverages smart contracts for fine-grained access control, employs Shamir secret sharing and re-encryption for assured key deletion, and utilizes Merkle Hash Tree (MHT) for public deletion verification. Notably, BBAD eliminates the need for a trusted third party, exhibits low computational overhead, supports customizable deletion time limit, and enables offline verification of deletion for users. Our experimental comparison with two prominent alternatives, Secure Electronic-Document Self-Destructing with Identity-Based Timed-Release Encryption (ESITE) and Key-Policy Attribute-Based Encryption for Assured Deletion (AD-KP-ABE), demonstrates that BBAD reduces data processing time by over 46.5%, data deletion time by 98.4%, and deletion verification time by 99.0%.
The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.
With the rapid growth of blockchain technology, smart contracts are now crucial to Decentralized Finance (DeFi) applications. Effective vulnerability detection is vital for securing these contracts against hackers and enhancing the accuracy and efficiency of security audits. In this paper, we present SimilarGPT, a unique vulnerability identification tool for smart contract, which combines Generative Pretrained Transformer (GPT) models with Code-based similarity checking methods. The main concept of the SimilarGPT tool is to measure the similarity between the code under inspection and the secure code from third-party libraries. To identify potential vulnerabilities, we connect the semantic understanding capability of large language models (LLMs) with Code-based similarity checking techniques. We propose optimizing the detection sequence using topological ordering to enhance logical coherence and reduce false positives during detection. Through analysis of code reuse patterns in smart contracts, we compile and process extensive third-party library code to establish a comprehensive reference codebase. Then, we utilize LLM to conduct an indepth analysis of similar codes to identify and explain potential vulnerabilities in the codes. The experimental findings indicate that SimilarGPT excels in detecting vulnerabilities in smart contracts, particularly in missed detections and minimizing false positives.
Blockchain technology leverages a cryptographic system to provide secure and immutable storage of transaction histories within a decentralised framework. While various industries have demonstrated interest in integrating blockchain into their IT systems, concerns regarding accessibility, privacy, performance, and scalability persist. Permissioned blockchain frameworks offer a viable solution for securing confidential records. Extensive research has been conducted to explore the opportunities, challenges, application areas, and performance evaluations of different public and permissioned blockchain platforms. Given the sensitive nature of medical information, healthcare organisations must adhere to various legal obligations, including HIPAA regulations, to protect these data. Although navigating these requirements can be challenging, it is crucial for safeguarding the reputation of healthcare providers, maintaining patient trust, and avoiding legal repercussions. Permissioned blockchains represent decentralised digital ledgers tailored to collaborate among businesses and organisations. Their popularity has increased significantly in recent years, resulting in the availability of several leading options, such as Hyperledger Fabric, Corda, Quorum, and MultiChain. Each of these platforms presents its own set of advantages and disadvantages. Although blockchain technology remains relatively nascent in the permissioned realm, several factors warrant consideration when comparing these platforms. This study will review the existing landscape of blockchain technologies in healthcare applications and identify the research scopes. This research aims to determine how permissioned blockchain technology can effectively fulfil the requirements for managing healthcare data.
One revolutionary way to tackle privacy and security issues in federated learning (FL) is to include blockchain technology and zero-knowledge proofs (ZK) into machine learning frameworks. To strengthen FL's defences against threats such as model poisoning attacks, this work investigates the use of ZK proofs. This study presents a new technique that uses secure multi-party computation (MPC) to efficiently detect poisoned models, addressing the shortcomings of previous ZK systems. Data anonymization, encryption of sensitive information, and encoding of categorical data all contribute to the proposed model's privacy-preserving features. Adding a privacy-protecting layer is an integral part of ML model integration. ZK circuits employ ZK-SNARKs or Bulletproofs to generate proofs that the ML model may use to predict without disclosing the data. ZK-SNARKs are trusted, and request validation and data access rules control proof access.
In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles. To avoid the storage of fake location information inside the smart contract, vehicles need to solve unique computational puzzles generated by the neighboring RSUs in a limited time frame whenever they need to report their location information. Assuming a vehicle has a single Central Processing Unit (CPU) and parallel processing is not allowed, it can solve a single computational puzzle in a given time period. With this approach, the vehicles with multiple fake identities are prevented from solving multiple puzzles at a time. In this way, we can mitigate a Sybil attack and avoid the storage of fake location information in a smart contract table. Furthermore, the RSUs maintain a dedicated blockchain for storing the location information of neighboring vehicles. They take part in mining for the purpose of storing the smart contract table in the blockchain. This scheme guarantees the privacy of the vehicles, which is achieved with the help of a PoL privacy preservation mechanism. The verifier can verify the locations of the vehicles without revealing their privacy. Experimental results show that the proposed mechanism is effective in mitigating Sybil attacks in VANET. According to the experiment results, our proposed scheme provides a lower fake location registration probability, i.e., lower than 10%, compared to other existing approaches.
Frequent user data breaches and misuse incidents highlight the flaws in current identity management systems. This study proposes a blockchain-based, peer-supervised self-sovereign identity (SSI) generation and privacy protection technology. Our approach creates unique digital identities on the blockchain, enabling secure cross-domain recognition and data sharing and satisfying the essential users' requirements for SSI. Compared to existing SSI solutions, our approach has the practical advantages of less implementation cost, ease of users' understanding and agreement, and better possibility of being soon adopted by current society and legal systems. The key innovative technical features include (1) using a zero-knowledge proof technology to ensure data remain "usable but invisible", mitigating data breach risks; (2) introducing a peer review mechanism among service providers to prevent excessive data requests and misuse; and (3) implementing a comprehensive multi-party supervision system to audit all involved parties and prevent misconduct.
Jianping Yu, Hang Yao, Kai Ouyang, Xiaojun Cao · 5 authors
Federated Learning (FL) enables clients to securely share gradients computed on their local data with the server, thereby eliminating the necessity to directly expose their sensitive local datasets. In traditional FL, the server might take advantage of its dominant position during the model aggregation process to infer sensitive information from the shared gradients of the clients. At the same time, malicious clients may submit forged and malicious gradients during model training. Such behavior not only compromises the integrity of the global model, but also diminishes the usability and reliability of trained models. To effectively address such privacy and security attack issues, this work proposes a Blockchain-based Privacy-preserving and Secure Federated Learning (BPS-FL) scheme, which employs the threshold homomorphic encryption to protect the local gradients of clients. To resist malicious gradient attacks, we design a Byzantine-robust aggregation protocol for BPS-FL to realize the cipher-text level secure model aggregation. Moreover, we use a blockchain as the underlying distributed architecture to record all learning processes, which ensures the immutability and traceability of the data. Our extensive security analysis and numerical evaluation demonstrate that BPS-FL satisfies the privacy requirements and can effectively defend against poisoning attacks.