Gabriel Chukwunonso Amaizu, Akshita Maradapu Vera Venkata Sai, Sanjay Bhardwaj, DongâSeong Kim · 6 authors
The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis. Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems. Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security, as they require the aggregation of sensitive information in a single location. Furthermore, these methods often suffer from limitations related to data diversity and scalability, hindering the development of universally robust diagnostic models. Recent advancements in machine learning, particularly deep learning, have shown promise in enhancing medical image analysis. However, the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations. This paper introduces FedViTBloc, a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning (FL) combined with Vision Transformers (ViT) and blockchain technology. The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques. By employing a decentralized FL approach, multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data. Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants. Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards, achieving 67% accuracy and reducing loss below 2 across 10 clients, ensuring scalability and robustness.
Open access
Artificial Intelligence in Healthcare and Education
Call detail records (CDRs) provide valuable insights into user behavior, which are instrumental for telecom companies in optimizing network coverage and service quality. However, while cloud computing facilitates clustering analysis on a vast scale of CDR data, it introduces privacy risks. The challenge lies in striking a balance between efficiency, security, and cost-effectiveness in privacy-preserving algorithms. To tackle this issue, we propose a privacy-preserving and cost-effective incremental density peak clustering scheme. Our approach leverages homomorphic encryption and order-preserving encryption to enable direct computations and clustering on encrypted data. Moreover, it employs reaching definition analysis to optimize the execution flow of static tasks, pinpointing the optimal junctures for transitioning between the two types of encryption to reduce communication overhead. Furthermore, our scheme utilizes a game theory-based verification strategy to ascertain the accuracy of the results. This methodology can be effectively deployed on the Ethereum blockchain via smart contracts. A comprehensive security analysis confirms that our scheme upholds both privacy and data integrity. Experimental evaluations substantiate the clustering accuracy, communication load, and computational efficiency of our scheme, thereby validating its viability in real-world applications.
Permissioned blockchains play a significant role in various application scenarios. Applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, namedDataFly. We design apeg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) theintegrated signature and public key encryptionscheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality ofDataFly, we implement a prototype ofDataFlyusing existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate thatDataFlyoutperforms related works in terms of transaction latency and gas costs.
These challenges were further amplified in recent years, causing recruitment processes to be more transparent in aspects like education, work experience, and professional verification. Traditional methods of validating credentials - contacting institutions or employers directly - can be time-consuming, mistake-prone and susceptible to fraud. Blockchain technology offers a solution that can assist in the digitization of credential verification, enabling employers to rapidly and securely verify a candidate's credentials. This paper explores blockchain use in talent acquisition and verification of credentials, highlighting its game-changing potential for the hiring process. Blockchain is a distributed and immutable digital ledger in which data cannot be altered once it has been entered, providing a high degree of trust and transparency. Blockchain technology enables educational institutions, employers and other credentialing bodies to issue verified digital credentials that are securely stored and can be easily presented to employers. One of the emerging trends is Verified CVs verified on blockchain based solutions, candidates can represent their validated qualifications on blockchain based solutions and employers can have instant access for any information regarding educational qualifications, certifications, experience regarding employments and skills related information. The blockchain based approach in the proposed credential verification process is because all the session information or credentials would be stored in a secured block so that respective academic institutions or bodies and their employers can verify using the cryptographic signatures as mentioned earlier. Each credential is identifiable and stored on the blockchain, which means that the record cannot be tampered with and can be verified from any interested party with valid access rights. As a result, they can also save time as smart contracts allow these individuals to execute the agreed-upon terms, provided certain conditions are satisfied, and avoid the need for intermediaries to obtain validated information. The key benefit of verification on blockchain is time and money. Verifying the authenticity of an employee goes through traditional checks like background checks and manual verification processes that take days or weeks which means a delay in hiring and increased costs of operation.
Qingyang Zhang, Shuai Qian, Jie Cui, Hong Zhong · 6 authors
Ensuring cloud data security and reducing cloud storage costs have become particularly important. Many schemes expose user file ownership privacy when deduplicating authentication tags and during integrity auditing. Moreover, key management becomes more difficult as the number of files increases. Also, many audit schemes rely on third-party auditors (TPAs), but finding a fully trustworthy TPA is challenging. Therefore, we propose a blockchain-based integrity audit scheme supporting data deduplication. It protects file tag privacy during deduplication of ciphertexts and authentication tags, safeguards audit proof privacy, and effectively protects user file ownership privacy. To reduce key management costs, we introduce identity-based broadcast encryption (IBBE) that does not require interaction with key servers, eliminating additional communication costs. Additionally, we use smart contracts for integrity auditing, eliminating the need for a fully trusted TPA. We evaluate the proposed scheme through security and theoretical analyses and a series of experiments, demonstrating its efficiency and practicality.
Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
The medical industry has made significant advancements in recent years. However, the lack of accountability in medical management has resulted in systemic deficiencies, which have adversely affected patient trust and contributed to an increase in medical disputes. As a result, there is a growing emphasis on managing the quality of medical services, particularly in enhancing patient experience. To address these challenges, we propose a new system for evaluating health services. This system will allow patients to anonymously rate the services they receive while also providing doctors the opportunity to appeal specific reviews. The hospital handles the evaluations and appeals through the management of the cloud platform. We propose a new scheme to assist the work of the platform, which is a lattice-based group signature with verifier-local revocation (VLR-GS). Most of the work on VLR-GS has focused on the random oracle model (ROM) or using non-interactive zero-knowledge proofs (NIZKs). Our construction is anonymous and traceable in the standard model under the hardness of the learning with errors problem and short integer solution problem. Furthermore, theoretically analyzing it has practical significance in both security and efficiency. In conclusion, the proposed scheme establishes a secure and privacy-oriented platform for an anonymous medical service evaluation system, with the goal of fostering patient trust and improving hospital service quality within the healthcare sector.
Federated Learning (FL), which permits decentralized model training without sharing raw data, guarantees âadherence to privacy laws like GDPR and HIPAA. This study offers a thorough analysis of FL with an âemphasis on its exceptional capacity to strike a balance between data value and privacy in industries âincluding healthcare, the Internet of Things, and finance. In contrast to previous evaluations, this study âexplores sophisticated privacy-preserving techniques, such as differential privacy and homomorphic âencryption, and assesses how well they work to handle issues like adversarial threats, non-IID data âdistributions, and communication overhead. The study also discusses the practical uses of optimization âtechniques like Federated Proximal (FedProx) and Federated Averaging (FedAvg). This paper provides âpractical insights and future approaches to promote the use of FL in privacy-sensitive AI applications by âcomparing and contrasting current methods and pointing out research gaps. FL is positioned as a ârevolutionary method for privacy-conscious machine learning because to this fresh viewpoint. This update highlights the paper's distinctive features that set it apart from prior reviews, including the âthorough examination of privacy mechanisms, assessment of optimization techniques, and identification âof research needsâ.
Ahod Alghuried, Mohammed Alkinoon, Manar Mohaisen, An Wang · 6 authors
Blockchain technology has heralded a new era in digital innovation, revolutionizing our approach to designing and building distributed applications in the digital sphere. Blockchain technology operates as an immutable digital ledger, where each entry representing a digital transaction is indelible and cannot be altered once established. Initially designed as the fundamental framework for cryptocurrencies, blockchain has outgrown its original purpose, demonstrating significant potential in various industries and offering a variety of security and privacy features. Our study provides a thorough and current survey of blockchain applications, security, privacy concepts, primitives, and threat models. It stands out by concentrating on how blockchain technology intersects with emerging fields like IoT, EVs, FinTech, and healthcare systems in a single framework. To provide security and privacy features, blockchain systems employ different foundational notions and primitives while tackling diverse adversarial scenarios with various capabilities and goals. This study presents a fresh examination of the current state of applications, security and privacy notions and primitives, and threat models in blockchain systems. Additionally, this work highlights existing gaps in knowledge and outlines open questions, aiming to stimulate interest in further advancements in the field.
With the further expansion of 5G networks, a main priority continues to shift towards secure and efficient protocols for data transmission. Traditional 5G security mechanisms, such as 3GPP AKA protocols, have limitations in scalability, latency, and resilience against cyber threats, making them quite unsuitable for complex high-density 5G environments. This study proposes a Secure Blockchain-based Data Transmission Protocol (SBDTP) with the decentralized and tamper-resistant feature of blockchain, combined with a hybrid consensus mechanism driven by Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT). In this respect, this study contributes to state-of-the-art research efforts in the field of enhancing data integrity, authentication, and confidentiality with reduced latency and energy consumption in 5G applications. Extensive simulations showed that SBDTP outperformed previous solutions by a large margin. This protocol reduces latency to 50-80 ms, increases throughput to 900 pps, allows up to 1000 nodes without performance degradation, and reduces energy consumption to 0.8 J per node. It also maintains a very close-to-perfection data integrity check rate of ~100% and a very minimal privacy loss rate of less than 1%, showing strong security that could serve well for real-time 5G applications such as IoT networks, autonomous vehicles, and smart cities. These results show that SBDTP offers an efficient and secure solution for data transmission over 5G networks, outperforming traditional and blockchain-based methods while fulfilling the tight requirements posed by next-generation networks. In the future, the protocol should be optimized for scalability, including further advanced privacy techniques to widen its adaptability to diverse 5G applications.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication and realâtime monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the context of blockchainâbased IoT systems. The study categorizes data aggregation algorithms into PrivacyâPreserving, Machine LearningâBased, Hierarchical, RealâTime, and Custom Aggregation Algorithms, each tailored to specific IoT requirements. PrivacyâPreserving Aggregation Algorithms focus on safeguarding sensitive data through encryption and secure protocols. Machine LearningâBased Aggregation adapts dynamically to data patterns, offering predictive insights and realâtime adaptability. Hierarchical Aggregation organizes devices into a structured hierarchy, optimizing data processing. RealâTime Aggregation processes data instantly, ensuring low latency for timeâsensitive applications. Custom Aggregation Algorithms are bespoke solutions tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques, this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research directions. The integration of blockchainâbased data aggregation techniques not only enhances IoT network efficiency but also ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 2022. Moreover, some important parameters such as privacy and security, latency, data processing, energy consumption, complexity, and reliability were involved in these investigations.
The proliferation of Internet of Things (IoT) devices in smart environments has created unprecedented challenges in identity management and security. Traditional centralized identity management systems face scalability, privacy, and single-point-of-failure issues when applied to IoT ecosystems. This paper presents a novel blockchain-based framework for decentralized identity management in smart IoT environments. Our proposed framework leverages blockchain technology's immutable ledger, smart contracts, and cryptographic mechanisms to provide secure, scalable, and privacy-preserving identity management for IoT devices. The framework incorporates a multi-layered security architecture that includes device authentication, access control, and identity verification mechanisms. Experimental results demonstrate that our approach achieves 99.7% authentication accuracy with reduced latency compared to traditional centralized systems. The framework also provides enhanced privacy protection through zero-knowledge proofs and selective disclosure mechanisms. This research contributes to the advancement of secure IoT identity management and provides a foundation for future developments in decentralized IoT security
Generative artificial intelligence models have brought about advancements in fields like healthcare and finance, as well as in autonomous systems; however, they also encounter notable security vulnerabilities, primarily when operating in cloud environments. These AI models can be targeted by attacks that involve altering input data to deceive the system into generating harmful or incorrect results. This study delves into the security issues that AI systems face in cloud setups, explicitly focusing on the dangers posed by adversarial manipulation of data integrity and the challenges of utilizing shared resources within multi-user environments. The text covers methods for defending AI models, like training and defensive distillation, to make them more robust against attacks. It also delves into security measures for the cloud, such as encrypted communications and robust authentication systems to safeguard data integrity. Furthermore, the importance of AI explainability and transparency in uncovering vulnerabilities and building trust is highlighted. The outcomes of security breaches emphasize the importance of having AI systems to avoid impacts on decision-making and broader ethical and societal concerns. The document also discusses research areas such as quantum algorithms and decentralized security structures to tackle evolving risks and safeguard the future of secure AI applications that generate content.
Abstract Blockchain-based data transaction protocols augmented with zero-knowledge proofs offer fairness to the participants, yet they encounter challenges pertaining to both security and efficiency. We propose the zk-DSTARK, a zk-STARK-based protocol that enables distributed generation of zero-knowledge proofs, significantly reducing the computational burden. And zk-DSTARK inherits zk-STARKâs feature of single proof generation for multiple uses, improving the efficiency of successive transactions. Furthermore, we propose a fair exchange system named zk-DCIAExchange for off-chain verification, which is based on zk-DSTARK and intel software guard extensions (SGX). This system not only minimizes on-chain overhead but also ensures the security and fairness of the transaction. Experimental results show that, in continuous transactions scenarios, the time overhead for subsequent transactions is diminished by 99.9% compared to the first transaction; compared to zero knowledge contingent payment (ZKCSP), our scheme achieves a remarkable 92% reduction in time overhead, and a 26.3% reduction when compared to FairSwap; with 32 distributed nodes and a trace length of 216, the proof generation time is reduced by ~85.45%; the additional verification time introduced by the SGX is ~0.45 s, which is deemed acceptable, and the on-chain verification overhead is reduced by ~7.2% compared to the ZKCSP and ~54.4% compared to FairSwap.
The advent of digital transformation in healthcare necessitates robust measures to secure electronic health records (EHR) against evolving cyber threats. The paper presents a comprehensive framework leveraging blockchain technology. In the proposed methodology, the use of blockchain, IoT, and wearable devices have been proposed. By using Ethereum as the platform of the blockchain the system checks for the integrity and confidentiality of the data. CNN helps in improving the Anomaly detection of patterns in EHR data, the Isolation Forest helps in the aspect of security of the data. The Proof of Authority (PoA) consensus mechanism elevates identity and reputation for the validation of transactions adding to the systemâs security. That is why the given results reveal the better performance of the "blockchain-based EHR system" compared to the existing healthcare systems, including Legacy Health Systems and Medicare Solutions that has 15% advantage. The proposed system achieves high efficiency level, with a measured efficiency of 96%. The framework, written in Python, stands for a privacy-friendly approach to HIM, and potential for new revolutionary means of healthcare data organization and patientsâ data protection.
As a critical component in federated learning (FL), secure aggregation enables the server to learn the aggregated model without observing clientsâ local training gradients. However, limited by computation and communication capabilities, existing aggregation schemes are not suitable to be directly employed in the Vehicular Ad Hoc Networks (VANETs) scenario. In this paper, we present a secure aggregation framework constructed with k-regular graph over VANETs scenario. We first optimize the secure aggregation scheme proposed by Bell et al. (CCS 2020). Specifically, using this new building block and an identity authentication mechanism in the vehicle-to-vehicle (V2V) communication mode, we design an optimized aggregation scheme that, when executed among n vehicles, can further reduce$2n$communication times between vehicles and the central server while guaranteeing logarithmic overhead. Besides, by applying a zero-knowledge proof to the authentication process, our proposal supports vehicles anonymously constructing the k-regular graph and completing parameter computation process, which enhances privacy preservation in semi-honest settings. Under the experiment and security analysis, our proposal is demonstrated to be able to effectively achieve privacy preservation while achieving less computation and communication overheads compared to state-of-the-art aggregation schemes.
Nadeem Yaqub, Jianbiao Zhang, Muhammad Irfan Khalid, Weiru Wang · 7 authors
Electronic health record transmission and storage involve sensitive information, requiring robust security measures to ensure access is limited to authorized personnel. In the existing state of the art, there is a growing need for efficient access control approaches for the secure accessibility of patient health data by sustainable electronic health records. Locking medical data in a healthcare center forms information isolation; thus, setting up healthcare data exchange platforms is a driving force behind electronic healthcare centers. The healthcare entities access rights like subject, controller, and requester are defined and regulated by access control policies as defined by the General Data Protection Regulation (GDPR). In this work, we have introduced a blend of policy-based access control (PBAC) system backed by blockchain technology, where smart contracts govern the intrinsic part of security and privacy. As a result, any Subject can know at any time who currently has the right to access his data. The PBAC grants access to electronic health records based on predefined policies. Our proposed PBAC approach employs policies in which the subject, controller, and requester can grant access, revoke access, and check logs and actions made in a particular healthcare system. Smart contracts dynamically enforce access control policies and manage access permissions, ensuring that sensitive data is available only to authorized users. Delineating the proposed access control system and comparing it to other systems demonstrates that our approach is more adaptable to various healthcare data protection scenarios where there is a need to share sensitive data simultaneously and a robust need to safeguard the rights of the involved entities.
Patient privacy data security is a pivotal area of research within the burgeoning field of smart healthcare. This study proposes an innovative hybrid blockchain-based framework for the secure sharing of electronic medical record (EMR) data. Unlike traditional privacy protection schemes, our approach employs a novel tripartite blockchain architecture that segregates healthcare data across distinct blockchains for patients and healthcare providers while introducing a separate social blockchain to enable privacy-preserving data sharing with authorized external entities. This structure enhances both security and transparency while fostering collaborative efforts across different stakeholders. To address the inherent complexity of managing multiple blockchains, a unique cross-chain signature algorithm is introduced, based on the Boneh-Lynn-Shacham (BLS) signature aggregation technique. This algorithm not only streamlines the signature process across chains but also strengthens system security and optimizes storage efficiency, addressing a key challenge in multi-chain systems. Additionally, our external sharing algorithm resolves the prevalent issue of medical data silos by facilitating better data categorization and enabling selective, secure external sharing through the social blockchain. Security analyses and experimental results demonstrate that the proposed scheme offers superior security, storage optimization, and flexibility compared to existing solutions, making it a robust choice for safeguarding patient data in smart healthcare environments.
The growing number of consumer Internet of Things (IoT) gadgets, including smart homes, fitness trackers, connected appliances, and home security systems, is transforming the way we live our daily lives. This has led to the emergence of a collaborative cloud-edge paradigm to leverage resources and services near the end-user, thereby providing prompt response to delay-sensitive real-time applications. Nevertheless, the tremendous amount of data generated by various IoT devices and sent over the network is always an open security challenge. The introduction of Federated Learning (FL) addresses the security and data privacy shortcomings of traditional centralized machine learning. Despite FLâs use for data privacy, it must overcome a number of significant challenges, such as privacy concerns, communication overhead, stragglers, and heterogeneity. To solve these challenges, this paper proposes a novel technique for enhancing security in IoT-enabled edge cloud computing networks, utilizing blockchain-driven FL and Gaussian Bayesian transfer convolutional neural network architectures for data analysis. Blockchain-driven FL ensures the security and privacy of consumer IoT applications. In comparison to state-of-the-art works, the experimental results achieved throughput of up to 89%, latency of 71%, training accuracy of 91%, validation accuracy of 96%, and network security of 92%.
Abstract Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.
Ahmed M. Tawfik, Ayman Al-Ahwal, Adly S. Tag Eldien, Hala H. Zayed
Abstract Advances in blockchain technology offer a decentralized ledger with transformative potential for healthcare data management, facilitating secure transactions and transparent record-keeping. Nevertheless, the sensitive nature of patient data requires enhanced privacy measures. This paper introduces a comprehensive framework enabling researchers to conduct collaborative statistical analysis on health records while preserving privacy and ensuring security. Statistics are invaluable across various disciplines, guiding consequential decisions based on such analysis. The framework integrates privacy-preserving techniques, including secret-sharing, secure multiparty computation (SMPC), and homomorphic encryption, within a blockchain-based healthcare ecosystem. Patient data is divided using secret-sharing, enabling controlled access. Furthermore, SMPC allows secure data aggregation without revealing individual records, while homomorphic encryption supports computation on encrypted data within smart contracts. Through a series of controlled experiments, we assess the frameworkâs effectiveness in maintaining data privacy, facilitating secure collaboration, and conducting statistical data analysis. The results demonstrate successful preservation of data privacy and secure analysis on a permissioned blockchain using the Hyperledger Fabric platform. Our framework showcases efficient performance while effectively utilizing system resources. This research contributes to the evolution of secure and privacy-conscious healthcare data analysis, paving the way for practical applications and future advancements.
Federated Learning (FL) has emerged as a promising paradigm that addresses the delicate balance between data-intensive model development and the preservation of user privacy. Unlike the conventional approach of aggregating large volumes of raw data in a single data center, FL conducts local training on various devices or institutional serversâsometimes referred to as âclientsââand only exchanges model parameters or gradients with a central entity. By design, this decentralized framework keeps personal or proprietary data within the confines of the originating device or organization, significantly reducing the chances of exposing sensitive information. A primary motivation for FL is the ever-increasing concern over privacy violations and compliance with stringent regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). As global data protection standards continue to evolve, FL offers a compelling solution by minimizing direct data sharing and thereby mitigating the risk of large-scale breaches. Beyond privacy considerations, FL holds practical appeal in many real-world scenarios, including healthcare, finance, the Internet of Things (IoT), and various consumer-focused applications. These sectors routinely handle confidential or regulated dataâmedical records, bank transactions, or user habitsâwhere a centralized data repository poses both security and compliance hazards. Nevertheless, FL also introduces its own set of challenges. Heterogeneous data distributions across clients can lead to biases and uneven training dynamics. Additionally, new threat vectorsâsuch as model poisoning and inference attacksâhave surfaced within decentralized training environments, prompting research into robust security strategies. Furthermore, practical implementation demands careful planning around communication overhead, computational capacity of clients, and the trade-offs that arise when adding privacy guarantees like Differential Privacy or Secure Multi-Party Computation. This paper explores the theoretical underpinnings of Federated Learning, reviews cutting-edge privacy-preserving techniques, examines potential security pitfalls, and presents real-world applications augmented by case studies. We also discuss performance evaluation methods crucial for determining FLâs viability and highlight upcoming research directions that can shape a secure, efficient, and privacy-centered AI ecosystem.
As an emerging paradigm in digital identity, Decentralized Identity (DID) appears advantages over traditional identity management methods in a variety of aspects, e.g., enhancing user-centric online services and ensuring complete user autonomy and control. Verifiable Credential (VC) techniques are used to facilitate decentralized DID-based access control across multiple entities. However, existing DID schemes generally rely on a distributed public key infrastructure that also causes challenges, such as context information deduction, key exposure, and issuer data leakage. To address the issues above, this paper proposes a issuer-hiding and privacy-preserving DID multi-party authentication model with a signature-less VC scheme, named SLVC-DIDA, for the first time. Our proposed scheme avoids the dependence on signing keys by employing hashing and issuer membership proofs, which supports universal zero-knowledge multi-party DID authentications, eliminating additional technical integrations. We adopt a novel zero-knowledge circuit to maintain the anonymity of the issuer set, thereby enabling public verification while safeguarding the privacy of identity attributes via a Merkle tree-based VC list. Furthermore, by eliminating reliance on a Public Key Infrastructure (PKI), SLVC-DIDA enables decentralized and self-sovereign DID authentication. Our experiments further evaluate the effectiveness and practicality of SLVC-DIDA.