Federated learning is an emerging distributed learning paradigm which brings an efficient and privacy-preserving intelligent model for the Internet of Vehicles (IoV). Unfortunately, federated learning is vulnerable to abnormal model attacks as it is hard to authenticate model parameters. Abnormal local models may slow down the convergence rate, reduce the accuracy of global models, and even deliberately control the global model in the attackers' chosen way. Furthermore, an abnormal global model may deduce sensitive information about vehicles and hinder the execution of genuine tasks. Therefore, in this paper, we propose a parameter-authentication federated learning (PAFL) scheme that can protect privacy of vehicles, such as driving habits, and defend against abnormal model attacks simultane-ously. Concretely, we equip the federated learning framework with the zero knowledge proof and Pedersen commitment to prove and authenticate the reliability of model parameters. Security and privacy analysis, as well as performance evaluation show that the PAFL scheme can successfully detect abnormal models with higher detection rate and achieve more secure global aggregation than existing representative schemes.
Minh-Duc Nguyen, Van Tong, Sami Souihi, Abdelhamid Mellouk
In the past, Quality of Service (QoS) was taken into account to evaluate the performance of multimedia services (e.g., video streaming, file transfer, etc.). However, it cannot reflect the user's perception, which is considered a crucial consideration by these services nowadays. Therefore, the emergence of Quality of Experience (QoE) is a potential solution. QoE can be measured via many parameters provided by Internet Service Providers (ISP), Application Service Providers (ASP), or end-users. However, privacy concerns hinder data sharing between the parties involved. To address these limitations, this paper proposes a QoE estimation mechanism that leverages Federated Learning. This mechanism aims to guarantee data privacy when no party needs to disclose their data to others. Moreover, the proposed mechanism incorporates the concept of a Decentralized Autonomous Organization (DAO) to mitigate the risk of a single point of failure in the centralized architecture of Federated Learning. It enables all participants to evaluate and select the model efficiently. The experimental results illustrate that the proposal surpasses the centralized solutions and guarantees data privacy.
This paper develops a cutting-edge multimodal federated learning framework, integrated with distributed ledger technologies, designed specifically for UAV delivery scenarios. The framework adopts various data modalities, including user pictures, behavior, and location, to dynamically optimize delivery routes and schedules, thus enhancing both user privacy and security of the delivery process. By employing federated learning, this framework allows data to be processed locally on individual devices, significantly enhancing both user privacy and data integrity. The integration of distributed ledger technology ensures that all updates to the federated model are not only immutable and traceable, but also secure. Through comprehensive evaluations, our framework shows outstanding improvements in both the efficiency and security of UAV deliveries. These findings show the transformative potential of our approach to establish user-centric, efficient, and secured UAV delivery systems.
Q. K. Mao, Sheng Wan, Daning Hu, Jiaqi Yan · 6 authors
This study proposes a novel privacy-preserving unsecured loan risk assessment system that allows decentralized finance (DeFi) lending platforms to offer loans without collateral. This system leverages federated learning methods to train risk assessment models using both off-chain and on-chain data sources, to more accurately evaluate borrower default risk for unsecured loans. Moreover, this system is built on a trusted execution environment (TEE) with program-level isolation, which provides a secure and efficient solution for DeFi platforms to offer unsecured loans. The effectiveness of this platform is validated through a set of simulation experiments. These experiments underscore the capability of the federated learning models to accurately assess borrower default risk while preserving stringent data privacy standards. The unique and innovative system design we proposed offers significant advancements for DeFi lending platforms. These improvements have the potential to greatly enhance DeFi platforms’ inclusiveness by offering unsecured loans while maintaining efficiency, and security.
Umer Majeed, Sheikh Salman Hassan, Zhu Han, Choong Seon Hong
In the rapidly evolving landscape of Web3 and blockchain technologies, decentralized autonomous organizations (DAOs) have emerged as innovative structures that operate autonomously through blockchain and smart contracts, eliminating the need for centralized control. The federated learning (FL) process, akin to an information flow under structured transparency, involves local models (LMs) as inputs and the global model (GM) as the output for each global iteration. The lack of transparency and security in traditional FL systems can be attributed to the centralized validation of LMs and GM updates. In this paper, we propose DAO-FL, a smart contract-based framework that leverages the power of DAOs to address these FL challenges. DAO-FL introduces the concept of DAO Membership Tokens (DAOMTs) as a governance tool within a DAO. DAOMTs play a crucial role within the DAO, facilitating members’ enrollment and expulsion. Our framework incorporates a Validation-DAO for decentralized input verification (DIV) of the FL process, ensuring reliable and transparent validation of LMs. Additionally, DAO-FL employs a multi-signatures approach facilitated by an Orchestrator-DAO to achieve decentralized GM updates, and thus decentralized output verification (DOV) of the FL process. We present a comprehensive system architecture, detailed execution workflow, implementation specifications, and qualitative evaluation for DAO-FL. Evaluation under threat models highlights DAO-FL’s out-performance against traditional centralized-FL, effectively countering input and output attacks. DAO-FL excels in scenarios where DIV and DOV are crucial, offering enhanced transparency and trust. In conclusion, DAOFL provides a compelling solution for FL, reinforcing the integrity of the FL ecosystem through decentralized decision making and validation mechanisms.
Vertical Federated Learning (VFL) effectively addresses the issue of data isolation, which makes data mining secure. Most VFL implementations rely on a single server or third party for training, which will be terminated if the server or third party fails. In addition, the model accuracy trained by VFL depends on the quality of the client’s local features; nevertheless, the client’s local feature quality is difficult to verify. There exists a chance that the features owned by the client are irrelevant to the model or the intermediate results submitted by the client are inaccurate, such that the model’s accuracy will be seriously affected. In order to solve the single point failure and model accuracy issues in VFL, this paper first proposes a Blockchain – based Decentralized VFL (BDVFL) training model. With the integration of blockchain and the VFL training process, the nodes within the blockchain are categorized into non-training and training nodes. Our method focuses on the scenario in which all training nodes possess labeled data and actively engage in the training procedure of VFL. To be specific, first, each client utilizes local features and initial models to carry out forward activation and generate intermediate results. Second, we randomly choose a training node and combine it with the intermediate results from all clients to formulate the loss function. Finally, each client updates the local model by using the gradient. To protect the raw features, a blinding factor is utilized for safeguarding the intermediate results submitted by the client, such that the training nodes cannot infer the local features from intermediate results. To mitigate the interference of irrelevant training outcomes from clients on the model’s accuracy, we propose a verifiable aggregation method to assess the validity of the intermediate results submitted by the clients. We have conducted both theoretical and experimental analysis, and the results demonstrate the effectiveness of the proposed method.
Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.
Federated learning (FL) has been widely adopted in various fields of study and business. Traditional centralized FL systems suffer from serious issues. To address these concerns, decentralized federated learning (DFL) systems have been introduced in recent years. With the help of blockchains, they attempt to achieve more integrity and efficiency. However, privacy preservation remains an uncovered aspect of these systems. To tackle this, as well as to scale the blockchain-based computations, we propose a zero-knowledge proof (ZKP)-based aggregator (zkDFL). This allows clients to share their large-scale model parameters with a trusted centralized server without revealing their individual data to other clients. We utilize blockchain technology to manage the aggregation algorithm via smart contracts. The server performs a ZKP algorithm to prove to the clients that the aggregation is done according to the accepted algorithm. Additionally, the server can prove that all inputs from clients have been used. We evaluate our approach using a public dataset related to the wearable Internet of Things. As demonstrated by numerical evaluations, zkDFL introduces verifiability of the correctness of the aggregation process and enhances the privacy protection and scalability of DFL systems, while the gas cost has significantly declined.
Outsourcing storage has emerged as an effective solution to manage the increasing volume of data. With the popularity of pay-as-you-go payment models in outsourcing storage, data auditing schemes that prioritize timeliness can be valuable evidence for elastic bill settlement. Unfortunately, existing data auditing schemes do not sufficiently consider timeliness during auditing. Furthermore, practical data auditing schemes should have the capability to check the integrity of scalable data. In this paper, we propose a blockchain-based dynamic data auditing scheme with strong timeliness to ensure that data stored in outsourcing storage systems remain intact. Our scheme encapsulates timestamps into homomorphic verifiable tags to simultaneously check data integrity and timestamp validity. To achieve dynamicity, we utilize the Merkle hash tree to store the tags, allowing for block-level dynamic operations. Additionally, by leveraging the transparency, non-repudiation, and tamper resistance of blockchain technology, we design a blockchain-based data auditing framework to prevent malicious behavior from all entities. We then formally prove the soundness and privacy of our scheme. Finally, we conduct theoretical analysis and experimental evaluation to demonstrate that the performance of our scheme is of acceptable efficiency to existing works in terms of computation cost, communication overhead, and storage overhead.
Xiaohu Du, Xiao Chen, Jialun Cao, Ming Wen · 6 authors
Federated learning (FL) is an emerging machine learning paradigm that aims to address the problem of isolated data islands. To preserve privacy, FL allows machine learning models and deep neural networks to be trained from decentralized data kept privately at individual devices. FL has been increasingly adopted in missioncritical fields such as finance and healthcare. However, bugs in FL systems are inevitable and may result in catastrophic consequences such as financial loss, inappropriate medical decision, and violation of data privacy ordinance. While many recent studies were conducted to understand the bugs in machine learning systems, there is no existing study to characterize the bugs arising from the unique nature of FL systems. To fill the gap, we collected 395 real bugs from six popular FL frameworks (Tensorflow Federated, PySyft, FATE, Flower, PaddleFL, and Fedlearner) in GitHub and StackOverflow, and then manually analyzed their symptoms and impacts, prone stages, root causes, and fix strategies. Furthermore, we report a series of findings and actionable implications that can potentially facilitate the detection of FL bugs.
In recent years, knowledge graphs (KGs) have been applied in various domains, where the construction and maintenance of the KGs are usually time- and labor-intensive. In this context, constructing shareable KG through multiple constructors is being attempted to reduce costs. In this collaborative process, security and quality issues are critical. The system for constructing shareable KGs should be capable to recover the KG from most malicious attack and to filter out wrong triples from dynamically submitted ones. Blockchain could naturally prevent malicious tampering with its record data, perfect for solving the security issue. However, the integration of multi-source KGs as well as the quality issue still lacks solutions. To address the issues, this paper proposes a blockchain-based high-quality KG collaborative construction framework to ensure the KG quality in its long-term evolution. The framework is built on the underlying consensus mechanism of the blockchain, adopted to an extensible data structure to store multi-source triples on the distributed ledger. A smart contract is implemented to publish triples, assess the contributor credibility and evaluate triple quality to keep the KG in high-quality. Anti-attack mechanisms are designed to defend against malicious triple submissions. Experiments are conducted demonstrating the effectiveness of the framework.
Hao Wang, Chunpeng Ge, Lu Zhou, Zhe Liu · 7 authors
Matrix computation is a crucial mathematical tool in scientific fields such as Artificial Intelligence and Cryptographic computation. However, it is difficult for resource-limited devices to execute large-scale matrix computations independently. Outsourcing matrix computation (OMC) is a promising solution that engages a cloud server to process complicated matrix computations for resource-limited devices. However, existing OMC schemes lack public verifiability, and thus resource-limited devices cannot verdict the correctness of the computing results. In this paper, for the first time, we propose a smart contract-based OMC scheme that publicly verifies the outsourcing matrix computation results. In our scheme, a smart contract running over the blockchain serves as a decentralized trusted third party to ensure the correctness of the matrix computation results. To overcome the Verifier's Dilemma in the blockchain, we present a blockchain-compatible matrix verification method that decreases the time complexity from$O(n^{3})$to$O(n^{2})$by utilizing a blinding method with the check digit and padding matrices. We make the verification become the form of comparing whether two results are identical rather than naive re-computing. Finally, we perform experiments on Ethereum and ARM Cortex-M4 and give in-depth analysis and performance evaluation, demonstrating our scheme's practicability and effectiveness.
With the widespread adoption of blockchain technology across diverse sectors[1], the imperative for efficient data storage and retrieval mechanisms has intensified[2]. This paper introduces an innovative approach that synergizes Zero-Knowledge Proofs (ZKP) and sharding techniques to bolster data privacy and optimize storage within blockchain systems. We present an exhaustive analysis of prevailing blockchain data storage mechanisms, pinpoint their inherent privacy vulnerabilities, and elucidate the potential of ZKP in mitigating these concerns. Furthermore, we propose a sharding-centric solution that not only fortifies data privacy but also augments the efficiency of data retrieval[3]. Experimental results, derived from real-world scenarios, validate the robustness and efficiency of our proposed methodology.
The rise of technology has brought with it a heightened awareness of the necessity to shield personal data and maintain exclusive access to specific knowledge. A notable solution that emerged from this consciousness is Zero-Knowledge Proofs (ZKPs) and, more specifically, Schnorr’s Protocol. Historically, Zero-Knowledge Proofs have a compelling lineage, tracing their roots back to the fervent discussions among cryptographers aiming to achieve a balance between information sharing and privacy. ZKPs are cryptographic methods that allow one party to prove to another that a statement is true, without revealing any specific information about the statement itself. In the midst of these developments, Schnorr’s Protocol emerged as a renowned interactive proof system. It possesses an intuitive structure that has made it pivotal in the enhancement of digital security. The typical flow of Schnorr’s Protocol begins with the prover sending a commitment to the verifier. The verifier then sends a random challenge back to the prover, who, in turn, produces a response. What’s captivating is that the verifier can ascertain the validity of the proof without gaining insight into the underlying secret. Interactive Schnorr’s Protocol involves real-time back-and-forth communication between the prover and verifier. On the other hand, the non-interactive version eliminates this need by using a cryptographic hash function, thereby streamlining the process.
Leon Witt, Usama Zafar, KuoYeh Shen, Felix Sattler · 7 authors
Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment [1]), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.
The exchange of patient medical records between healthcare providers is essential for quality care but faces challenges related to privacy, security, and centralized control. This article presents the development of a decentralized medical data exchange system using Distributed Ledger Technology based on the IOTA Tangle. The system architecture and core components for immutable storage and transmission of medical records are described. The system uses masking and encryption techniques to maintain patient privacy while allowing healthcare providers to access full records with patient consent. The IOTA Tangle enables fee-less transactions and data integrity verification via its Directed Acyclic Graph structure. System validation through simulation experiments demonstrates the ability to securely share medical data at scale with lower resource costs compared to traditional blockchain designs. The system proves the viability of a decentralized, self-sovereign approach for efficient and confidential medical data sharing using Distributed Ledger Technology. The secure data exchange platform can enable new models of care and research while maintaining patient privacy and healthcare ethics.
Muhammad Hasnain, Fahad R. Albogamy, Saeed S. Alamri, Imran Ghani · 5 authors
The Hyperledger Fabric (HF) framework is widely studied for securing electronic health records (EHRs) in the healthcare sector. Despite the various cross-domain blockchain technology (BCT) applications, little is known about the role of the HF framework in healthcare. The purpose of the systematic literature review (SLR) is to review the existing literature on the HF framework and its applications in healthcare. This SLR includes literature published between January 2015 and March 2023 in the ACM digital library, IEEE Xplore, SCOPUS, Springer, PubMed, and Google Scholar databases. Following the inclusion and exclusion criteria, a total of 57 articles emerged as eligible for this SLR. The HF framework was found to be useful in securing health records coming from the Internet of Medical Things (IoMT) and many other devices. The main causes behind using the HF framework were identified as privacy and security, integrity, traceability, and availability of health records. Additionally, storage issues with transactional data over the blockchain are reduced by the use of the HF framework. This SLR also highlights potential future research trends to ensure the high-level security of health records.
Many IoT applications require users to share their devices’ location, and enhanced privacy-protection means sharing location anonymously, unlinkably and without relying on any administrators. But under such protection, it is difficult to trust shared location data, which may be from unregistered devices or from the same one’s multiple logins or from the cloned device ID, even be generated by an attacker without any devices! Such untrusted location sharing cheats system, misleads users, even attacks system. To the best of our knowledge, such problems have not been solved in a decentralized system. To solve them in one scheme, we put forward the first decentralized accumulator for device registration and construct the first practical decentralized anonymous authentication for device login. When logging in, the device provides a special knowledge proof, which integrates zero-knowledge (for privacy) with knowledge-leakage (for identifying abnormal behaviors) designing for blockchain (for decentralization). Therefore, in our system, only registered IoT devices can upload location data and their logins are anonymous and unlinkable, while login exceeding${K}$times in a system period or cloning ID to login concurrently can be identified and tracked without any trusted centers. In addition, we provide the security proofs and the application examples of the proposed scheme. And the efficiency analysis and experimental data show that the performance of our scheme can meet the needs of real-world location sharing on IoT.
Kang Jieying, Lihua Zhao, Yang Qi, Lei Mingyang · 6 authors
With the popularization of computer and network technology, data with proof and legal effect has gradually changed from physical to electronic, and electronic documents have gradually become an important carrier and tool for transmitting information and recording facts. Electronic evidence has the characteristics of easy storage and high security, which also imposes high requirements on network security and other aspects. How to effectively preserve electronic evidence and fully trace its source when necessary has become an important research issue. This paper proposes a method to achieve efficient preservation and traceability of electronic evidence through the combination of blockchain, Image-Caption and word segmentation technology. When using blockchain technology to ensure the non-tampering and anti-repudiation of electronic evidence, ensure that electronic evidence While improving the authenticity and integrity of electronic evidence, it improves the traceability and accuracy of electronic evidence.
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been designed to solve issues like single point failure and data falsification brought by centralized FL paradigm. Moreover, it is easier to allocate incentives to nodes with the help of the blockchain. Various centralized federated learning frameworks like FedML, have emerged in the community to help boost the research on FL. However, decentralized blockchain-based federated learning framework is still missing, which cause inconvenience for researcher to reproduce or verify the algorithm performance based on blockchain. Inspired by the above issues, we have designed and developed a blockchain-based federated learning framework by embedding Ethereum network. This report will present the overall structure of this framework, which proposes a code practice paradigm for the combination of FL with blockchain and, at the same time, compatible with normal FL training task. In addition to implement some blockchain federated learning algorithms on smart contract to help execute a FL training, we also propose a model ownership authentication architecture based on blockchain and model watermarking to protect the intellectual property rights of models. These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain). The source code is avaliable on https://github.com/GTMLLab/VeryFL.