Blockchain Papers

Namya Aankur Gupta, Mansi Bansal, Seema Sharma, Deepti Mehrotra · 5 authors

No abstract is available for this record.

Zhenwen Peng, Yingjie Song, Qiong Wang, Xiong Xiao · 5 authors

The integration of blockchain technology into the federated learning (FL) process offers effective measures for safeguarding the security and privacy of model data. However, the inherent consensus mechanism of blockchain technology can introduce long latency, which may hinder the overall efficiency of FL. To address this challenge, we propose a blockchain-based FL training strategy that tackles the following issues: (1) Reducing the number of parameter aggregations in the blockchain network by increasing the number of local training epochs, which effectively minimize the frequency of blockchain authentication and packing operations. (2) Mitigating communication overhead between terminals and edge nodes in the blockchain network by leveraging the wait-free backpropagation technique, which reduces the communication overhead. Experimental results demonstrate that our proposed strategy yields improvements in both convergence efficiency and system scalability.

Zihou Zhang, Jiangtao Li, Yufeng Li, Yuanhang He

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.

Tianxiu Xie, Keke Gai, Jing Yu, Liehuang Zhu

Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated model commercialization to reinforce the model performance, including licensing or trading Deep Neural Network (DNN) models. However, DNN model trading may violate the benefit of the model owner due to unauthorized replications or misuse of the model. Model identity auditing is a challenging issue in protecting DNN model ownership, and verifying the integrity and ownership of models is one of the critical obstacles. In this paper, we focus on the above issue and propose an \underline{A}ccumulator-enabled \underline{A}uditing for \underline{D}ecentralized \underline{Id}entity of DNN \underline{M}odel (A2-DIDM) that utilizes blockchain and zero-knowledge techniques to protect data and function privacy while ensuring the lightweight on-chain ownership verification. The proposed model presents a scheme of identity records via configuring model weight checkpoints with zero-knowledge proofs, which incorporates predicates to capture incremental state changes in model weight checkpoints. Our scheme ensures both computational integrity and programmability in DNN training process so that the uniqueness of the weight checkpoint sequence in a DNN model is preserved. %to ensure the correctness of model identity auditing, so that the uniqueness of the weight checkpoint sequence in a DNN model is preserved. A2-DIDM also addresses privacy protections in decentralized identity. We systematically analyze the security and robustness of our proposed model and further evaluate the effectiveness and usability of auditing DNN model identities. The code is available at https://github.com/xtx123456/A2-DIDM.git.

Gopinath Ganapathy, Sujatha Jamuna Anand, M. Jayaprakash, S. Lakshmi · 6 authors

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in a IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16% for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

Yuqing Xu, Guangxia Xu, Guangxia Xu, Yuan Liu · 6 authors

No abstract is available for this record.

Akinul Islam Jony, Mubashir Mohsin

Federated learning (FL) has emerged as a viable paradigm for decentralized machine learning (DML) across multiple platforms while safeguarding data privacy.This study covers a thorough analysis of FL strategies intended to protect the privacy of data.It investigates the techniques and tactics FL uses to secure data privacy and explores the benefits and constraints of FL privacy protection.Using a methodical approach to the literature review, the study distinguishes FL approaches, explores the nuances of the FL transfer process, assesses current techniques, and identifies inherent vulnerabilities and shortcomings.These outcomes emphasize the vitality FL has for alleviating concerns about privacy while fostering collaborative learning.A variety of FL techniques are identified in the review, each of which contributes a distinct mechanism for maintaining privacy.These include differential privacy, homomorphic encryption, pruning, secure aggregation, secure multiparty computation, and zero-knowledge proofs, among others.This study provides scholars and practitioners with significant perspectives on existing procedures and prospective areas for advancement by integrating ideas from multiple sources to provide an overview of the current FL landscape concerning data privacy protection.The findings are more credible and reliable because of the systematic study, which also provides a strong basis for further research on FL and data privacy protection.At the end of the study, the implications of FL approaches for improving data privacy are covered.The significance of continuing research endeavors to tackle new problems and refine FL techniques for resilient and expandable privacy protection in the distributed machine learning age is underlined.

S.B. Gurumurthy, Ajit Danti

No abstract is available for this record.

R. Raja Sekar, Abhiram Masna, Sagar Sharma, Aman Abraham · 5 authors

In this transformative initiative, Identity and Access Management (IAM) undergoes a profound evolution through decentralized blockchain technology. The project focuses on developing a user-centric identity wallet, leveraging blockchain's attributes for transparency, immutability, and cryptographic security, orchestrating smart contracts to automate identity verification and access control. This decentralized IAM architecture not only fortifies security but also empowers users with ownership and sovereignty over their personal data, ensuring a tamper-resistant environment and fostering a more resilient and user-centric digital landscape. The project's strategic alignment with interoperability standards amplifies its impact, promising to set new benchmarks for secure and efficient IAM frameworks across industries.

Yizhong Liu, Yizhong Liu, Zixu Jiang, Zixu Jiang · 8 authors

No abstract is available for this record.

Mikail Mohammed Salim, Laurence T. Yang, Jong Hyuk Park

No abstract is available for this record.

Yue Zhang, Shouqiao Wang, Sijun Tan, Xiaoyuan Liu · 6 authors

This paper introduces the Proof of Sampling (PoSP) protocol, a Nash Equilibrium-based verification mechanism, and its application to decentralized machine learning inference through spML. Our protocol has a pure strategy Nash Equilibrium, compelling rational participants to act honestly. It economically disincentivizes dishonest behavior, making it costly for participants to compromise the network's integrity. In our spML protocol, we apply PoSP to decentralized inference for AI applications via a novel cryptographic protocol. The resulting protocol is much more efficient than zero knowledge proof based approaches. Moreover, we anticipate that the PoSP protocol could be effectively utilized for designing verification mechanisms within Actively Validated Services (AVS) in restaking solutions. We further expect that the PoSP protocol could be applied to a variety of other decentralized applications. Our approach enhances the reliability and efficiency of decentralized systems, paving the way for a new generation of decentralized applications.

Akhmad Maariz, Muhammad Aqil Wiputra, Muhammad Randika Dafa Armanto

This study explores the transformative impact of blockchain technology on data integrity and security in digital environments. Through a comprehensive assessment of data integrity metrics across prominent blockchain networks, including Bitcoin, Ethereum, and Hyperledger Fabric, we unveil nuanced differences in immutability and reliability. Our security analysis delves into the cryptographic strength and resistance to unauthorized access, showcasing the outstanding security features of Hyperledger Fabric and Bitcoin, with Ethereum exhibiting commendable yet moderate security levels. The discussions underscore the multifaceted nature of blockchain technology, emphasizing the importance of selecting a platform aligned with specific use cases. Hyperledger Fabric and Bitcoin emerge as strong contenders for applications requiring high integrity and robust security, while Ethereum offers a reliable but moderate alternative. As blockchain technology continues to evolve, this study provides valuable insights for practitioners and researchers, guiding the strategic selection of blockchain platforms to harness their transformative potential in diverse digital environments.

Collin Meese, Hang Chen, Wanxin Li, Danielle Lee · 7 authors

Managing urban traffic dynamics is critical in Intelligent Transportation Systems (ITS), where short-term traffic prediction is vital for effective congestion management and vehicle routing. While existing centralized deep learning (DL) models have achieved high prediction accuracy, their applicability is limited in decentralized ITS environments. The increasing use of connected vehicles and mobile sensors has led to decentralized data generation in ITS, presenting an opportunity to improve traffic prediction through collaborative machine learning. Recently, blockchain technology has shown promise in improving ITS efficiency, security, and reliability. In conjunction with blockchain, Federated Learning (FL) is a suitable approach to leverage online data streams in ITS; however, most research on FL for traffic prediction focuses on offline learning scenarios. This paper researches a blockchain-enhanced architecture for training online traffic prediction models using FL. The proposed approach enables decentralized model training at the edge of the ITS network, and extensive experiments used dynamically collected arterial traffic data shards as a case study to evaluate online learning performance. The results demonstrate that our online FL approach outperforms the per-device, non-federated baseline models for most sensors while maintaining a suitable execution time and latency for real-world deployment.

Dr. Amina Qureshi

In the era of increasing government surveillance driven by national security and law enforcement interests, maintaining individual privacy has become a critical challenge. Cryptographic privacy solutions offer powerful tools to protect communication confidentiality, data integrity, and user anonymity against intrusive surveillance mechanisms. This paper explores the contemporary cryptographic techniques employed to counter government surveillance efforts, including end-to-end encryption, anonymous communication networks, zero-knowledge proofs, and homomorphic encryption. We also analyze the legal and ethical landscape shaping the deployment of these technologies. Emphasis is placed on the balance between privacy preservation and regulatory oversight. Case studies illustrate practical implementations and limitations. The findings highlight the necessity of advancing cryptographic solutions while addressing usability and policy challenges for robust privacy protection.

Prakash Tekchandani, Abhishek Bisht, Ashok Kumar Das, Neeraj Kumar · 7 authors

With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in big data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current big data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.

Trent Menard, Mahmoud Abouyoussef

Vehicle digital forensics (VDF), encompasses the investigation of digital evidence related to vehicles, plays a crucial role in modern transportation systems, aiding in accident investigations, crime detection, and ensuring road safety. However, the need to collect data for such investigations has exacerbated privacy concerns, as sensitive vehicular data is susceptible to unauthorized access and exploitation. While blockchain technology has been explored in the literature to address these challenges, existing techniques often prioritize user anonymity over data unlinkability, limiting their effectiveness in preserving privacy. In response, this paper proposes a novel blockchain-based networking strategy for VDF, aiming to achieve both user anonymity and data unlinkability concurrently. By lever-aging group signatures and secure communication protocols, the proposed strategy ensures the integrity of vehicular data while mitigating privacy risks. Performance evaluations demonstrate the efficacy of the strategy in terms of computation and communication overheads, while comparative analyses highlight its superiority over existing approaches in terms of privacy preservation and security.

S. Sabari, N. V. Keerthana

This project focuses on Zero-Knowledge Proofs (ZKPs), a groundbreaking cryptographic technique reshaping data authentication while preserving maximum confidentiality.ZKPs enable the verification of truthfulness in statements without disclosing associated data, ensuring the utmost protection of sensitive information.With applications spanning various domains, including secure authentication protocols, privacy-preserving transactions in decentralized systems like blockchain, and confidential data verification across digital interactions, ZKPs offer versatile solutions for secure communications.The project aims to safeguard sensitive business information during outsourcing service processes.The implementation of ZKPs intends to establish a secure communication framework that fosters trust among stakeholders without compromising sensitive details, ensuring enhanced confidentiality in outsourced operations.At its core, ZKPs empower a prover to convince a verifier of a statement's validity without revealing underlying data, establishing an unmatched level of security and privacy.This concept shields against unauthorized access and data breaches, fostering trust between entities without the exchange of sensitive details.The versatility of ZKPs extends beyond authentication, influencing secure voting systems, safeguarding digital identities, and facilitating confidential transactions while upholding user privacy.

Chunlin Li, Yong Zhang, Jianyang Wu, Youlong Luo · 5 authors

Intelligent Connected Vehicles (ICVs) need to obtain real-time traffic data from nearby ICVs or remote content providers to ensure safe driving. However, providers are hesitant to share their data due to privacy and benefits concerns. To ensure privacy while improving efficiency of obtaining data, we proposed smart contract-based data sharing among ICVs, and content delivery between ICVs and remote content provider. To solve low willingness to vehicles due to untrustworthy third-party platforms, we use smart contracts to implement access control during data upload and transaction. Then, we propose a one-to-many sharing model based on Stackelberg game to model the interaction between consumers and owners. Consumers adjust their reward strategies with the owners’ optimal strategies to maximize its utility, thus obtaining the nash equilibrium solution. To provide reliable quality of service (QoS) and security guarantee for content delivery, smart contracts regulate the delivery process, facilitating automatic execution under specific conditions. Transaction records audited and stored on blockchain enhance transparency and trustworthiness. Utilizing a delivery utility model that considers benefits, costs, and mining profits, proposed quantum particle swarm optimization (QPSO) algorithm is used to find the optimal solution. We built an EdgeChain testbed, and used BDD-100K dataset to evaluate the performance in utility, access delay, etc. Compared to CTM and MFPA, proposed data sharing algorithm achieves maximum consumer utility. Compared to LRU, PCCM and MARL, when content is 400, proposed content delivery algorithm reduces average access delay by 30.88%, 18.92% and 4.86%, and reduce backhaul load by 50.04%, 47.23% and 3.16%.

Gabin Heo, Inshil Doh

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology and the computing infrastructure. However, security problems arise as data utilization increases. If the sensing data from internet of things devices are constantly exposed, the users’ private information can be determined, a critical security risk that could result in privacy breaches. This paper proposes a secure data processing system using the blockchain and differential privacy for data security and privacy protection in urban computing. When a service provider requests information, the system generates it from urban computing data using machine learning. We apply differential privacy to these data to protect privacy. However, if a query repeats, differential privacy may provide insufficient privacy protection. Therefore, we reduce the total privacy cost by reusing noise for the same data and privacy parameters using the blockchain. Machine learning accuracy may decrease when noisy data are used for training. Thus, we increase accuracy by storing and appropriately using the model parameters generated by the same data in the blockchain. We design, simulate, and analyze the results of an experimental environment for reusing noise for differential privacy and parameter utilization of machine learning using the blockchain. The proposed approach reduces privacy costs compared to the existing mechanism while protecting data privacy. We demonstrate that, through parameter utilization, the accuracy improves compared to conventional mechanisms.

Hsia‐Hung Ou, C. C. Pan, Yang-Ming Tseng, Iuon‐Chang Lin

FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2’s support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user’s device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution.

Jens Ernstberger, Chengru Zhang, Luca Ciprian, Philipp Jovanovic · 5 authors

We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only $64$ constraints per multiplication for $2^{15}$ single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has $15.9 \times$ less constraints utilizing single precision floating-point values, and $12.2 \times$ less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing - Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a configuration, Bob can create a proof of (non-)proximity in $0.26 s$, whereas Alice can verify her distance to about $470$ peers per second

Vijaykumar, Patil Pratik, Prerna Tulsiani, Sunil B. Mane

Public Cloud Computing has become a fundamental part of modern IT infrastructure as its adoption has transformed the way businesses operate. However, cloud security concerns introduce new risks and challenges related to data protection, sharing, and access control. A synergistic integration of blockchain with the cloud holds immense potential. Blockchain's distributed ledger ensures transparency, immutability, and efficiency as it reduces the reliance on centralized authorities. Motivated by this, our framework proposes a secure data ecosystem in the cloud with the key aspects being Data Rights, Data Sharing, and Data Validation. Also, this approach aims to increase its interoperability and scalability by eliminating the need for data migration. This will ensure that existing public cloud-based systems can easily deploy blockchain enhancing trustworthiness and non-repudiation of cloud data.

Chaehyeon Lee, Jonathan Heiss, Stefan Tai, James Won‐Ki Hong

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.