The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture, which provides substantial support for data sharing and data privacy protection. Nevertheless, achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues. To address the above problems, we propose a blockchain-assisted federated learning-driven support vector machine (BAFL-SVM) framework to realize efficient data sharing and privacy protection. The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module. Specifically, in FedSVM-RiceCare, we utilize federated learning and SVM to train the model, improving the accuracy of the experiment. Then, in FedPrivChain, we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them. Finally, we conduct a large number of experiments on a real-world dataset of rice pests and diseases, and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.
In today's data-driven world, the convergence of advanced machine learning techniques with privacy concerns has prompted the development of innovative approaches to safeguard sensitive information while harnessing the power of data analytics.This research article delves into the realm of privacy-preserving machine learning algorithms, specifically focusing on methodologies that embrace the concept of local information privacy.The abstract provides a succinct overview of the key themes, methodologies, and implications elucidated within the paper.The abstract begins by contextualizing the contemporary landscape, emphasizing the proliferation of big data and the attendant privacy challenges it poses.It highlights the dichotomy between the utility of machine learning algorithms and the imperative of preserving individuals' privacy, setting the stage for exploring novel solutions.Central to the abstract is the conceptual framework of local information privacy, which forms the cornerstone of privacy-preserving machine learning algorithms discussed in the paper.The abstract delineates the theoretical foundations of this framework, elucidating how decentralized computation and differential privacy principles contribute to safeguarding sensitive data.Moving beyond theoretical underpinnings, the abstract provides insights into the methodologies employed in privacy-preserving machine learning.It outlines diverse approaches such as federated learning, secure multi-party computation, and homomorphic encryption, showcasing their utility in mitigating privacy risks while enabling collaborative model training and inference.Furthermore, the abstract underscores the practical implications of adopting privacy-preserving machine learning algorithms leveraging local information privacy.It cites examples across various sectors, including healthcare, finance, and IoT, where decentralized learning frameworks empower organizations to derive actionable insights from data while upholding privacy regulations and ethical standards.The abstract concludes by delineating potential avenues for future research and development in the field.It emphasizes the importance of scalability, efficiency, and robustness in privacy-preserving techniques, calling for interdisciplinary collaborations to address emerging challenges and navigate regulatory landscapes effectively.The abstract encapsulates the essence of the research article, providing a concise yet comprehensive overview of privacy-preserving machine learning algorithms using local information privacy.It serves as a gateway for readers to delve deeper into the nuances of the topic while highlighting its significance in addressing contemporary privacy challenges in the era of big data and advanced analytics.
Smart contracts, leveraging the power of blockchain technology, have revolutionized the execution and enforcement of agreements. However, their adoption also brings forth substantial challenges in terms of security and privacy. This research paper aims to identify the recent areas of focus and provide a comprehensive perspective on blockchain applications and smart contracts, highlighting their main issues and corresponding solutions. Furthermore, it seeks to address the gaps in current research and outline future avenues of investigation. The primary objective is to assess the security and privacy concerns associated with smart contracts in blockchain and propose effective measures to enhance their robustness. By conducting a thorough analysis of vulnerabilities, attack vectors, and privacy considerations, this study offers valuable insights into the risks involved in smart contracts. It also puts forth practical solutions and best practices to mitigate these risks, ensuring a more secure and privacy-preserving environment for the deployment and execution of smart contracts.
In multi-stakeholder systems, such as healthcare, the Internet of Things, and supply chain management, there is frequent data generation, exchange, and sharing. As a result, data owners often desire control over their data and maintain privacy, while data consumers require methods to ascertain the origins and creators of the data. These conflicts of interest require developing data governance systems that guarantee data provenance, privacy protection, consent management, and selective disclosure. This research proposed a decentralized data governance system utilizing blockchain technology, proxy re-encryption (PRE), and Boneh, Boyen, and Shacham (BBS) signatures to address these challenges. The proposed system enables data owners to control, selectively share, and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms while also allowing data consumers to understand the lineage of the data through a blockchain-based provenance mechanism. As a case study, the research examined and evaluated electronic prescriptions involving sensitive data and multiple stakeholders, including patients as data owners and doctors and pharmacists as data consumers. The research was structured as a collection of published articles organized in the following sequence: problem formulation and developing smart contracts, implementing privacy and consent management through PRE, and applying BBS signatures for selective data sharing. The proof-of-concept implementation and evaluations, conducted using CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures, demonstrate that the proposed decentralized system is platform-agnostic, scalable, and capable of providing a higher level of transparency, privacy, and trust with minimal overhead.
This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient privacy. To achieve this, the study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes. The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility. Additionally, blockchain technology ensures secure and transparent aggregation and storage of model updates. Experimental results on the SVHN dataset demonstrate that the proposed framework achieves strong privacy guarantees against various attack scenarios while maintaining high accuracy in health analytics tasks. For 15 rounds of federated learning with an epsilon value of 8.0, the model obtains an accuracy of 64.50%. The blockchain integration, utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction latency of around 6 seconds and consistent gas consumption across rounds, validating the practicality and feasibility of the proposed approach.
This article introduces a new asynchronous Byzantine-tolerant asset transfer system (cryptocurrency) with three noteworthy properties: quasi-anonymity, lightness, and consensus-freedom. Quasi-anonymity means no information is leaked regarding the receivers and amounts of the asset transfers. Lightness means that the underlying cryptographic schemes are \textit{succinct}, and each process only stores data polylogarithmic in the number of its own transfers.Consensus-freedom means the system does not rely on a total order of asset transfers. The proposed algorithm is the first asset transfer system that simultaneously fulfills all these properties in the presence of asynchrony and Byzantine processes. To obtain them, the paper adopts a modular approach combining a new distributed object called agreement proofs and well-known techniques such as vector commitments, universal accumulators, and zero-knowledge proofs. The paper also presents a new non-trivial universal accumulator implementation that does not need knowledge of the underlying accumulated set to generate (non-)membership proofs, which could benefit other crypto-based applications.
In this study, we delve into cutting-edge solutions for security-centric, privacy-enhanced federated learning, a rapidly evolving area of research that bridges the gap between data privacy and collaborative machine learning. Our analysis offers a comprehensive comparative evaluation of existing methodologies, shedding light on the strengths and limitations of current approaches. By introducing new perspectives, we aim to push the boundaries of secure federated learning, exploring techniques that enhance data protection without compromising learning efficiency. Additionally, we highlight emerging challenges and opportunities in the field, emphasizing the importance of scalable, privacy-preserving mechanisms in decentralized systems. As federated learning continues to gain traction across various sectors such as healthcare, finance, and IoT, our study serves as a foundation for future research, identifying key areas for innovation and improvement. This forward-looking approach ensures that federated learning can continue to evolve as a trustworthy and robust solution for privacy-sensitive applications, addressing both current and future security concerns.
Aulia Arif Wardana, Grzegorz Kołaczek, Parman Sukarno
This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.
Jesús García-Rodríguez, Stephan Krenn, Jorge Bernal Bernabé, Antonio Skármeta
The increasing user awareness and regulatory framework (e.g., GDPR, eIDAS2) have contributed to considering data minimization and privacy-by-design as central guiding principles for new systems. Among others, this has led to a paradigm shift towards Self-Sovereign Identity solutions to put the user in full control over their data. Despite the promising landscape, privacy-preserving Attribute-Based Credentials (p-ABC) have not been widely adopted, mainly due to the lack of secure, flexible and efficient implementations that cover the basic and advanced needs in p-ABC systems. In this work, we tackle this gap by developing an improved zero-knowledge showing protocol of a distributed p-ABC scheme based on Pointcheval-Sanders Multi-Signatures to allow for modular extensions through commit-and-prove techniques. We use it to implement a flexible p-ABC system with decentralized issuance that, apart from the basic notions of p-ABCs, covers range proofs, pseudonyms, inspection and revocation. Lastly, we thoroughly evaluate the performance of the system under different testbed conditions, showing a significant efficiency improvement over previous implementations.
Syed Thouheed Ahmed, T R Mahesh, E. Srividhya, V. Vinoth Kumar · 7 authors
Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.
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.
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.
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.
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.
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.
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.
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.
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) to enhance UCB. zkUCB is carefully designed to safeguard the confidentiality of training data and algorithmic parameters, ensuring transparent UCB decision-making. Experiments highlight zkUCB's superior performance, attributing its enhanced reward to judicious quantization bit usage that reduces information entropy in the decision-making process. zkUCB's proof size and verification time scale linearly with the execution steps of zkUCB. This showcases zkUCB's adept balance between data security and operational efficiency. This approach contributes significantly to the ongoing discourse on reinforcing data privacy in complex decision-making processes, offering a promising solution for privacy-sensitive applications.