Search Results (165)

The rapid growth of multi-source, multi-perspective data in healthcare, finance, and social media has increased the need for unsupervised learning methods that integrate diverse views while preserving data privacy and locality. Federated learning (FL) enables collaborative model training without sharing raw data, yet its application to multi-view clustering (MVC) remains underdeveloped. This survey presents the first comprehensive, PRISMA-guided systematic review of federated multi-view clustering (FedMVC). A multidimensional taxonomy classifies existing studies according to FL paradigms (horizontal, vertical, and federated transfer learning); clustering approaches (hard/soft, spectral, density-based, deep, tensor, and non-negative matrix factorization [NMF]-based); aggregation strategies (FedAvg, FedProx, FedOpt, and adaptive optimizers); privacy mechanisms (differential privacy, secure multi-party computation, homomorphic encryption, and trusted execution environments); and non-IID data-handling techniques. Centralized and federated objective functions are examined, with comparisons of computational complexity, communication overhead, privacy guarantees, and scalability. Practical implementation guidance and applications in healthcare, finance, and cross-platform social media are also discussed. Key research challenges include formal privacy–utility trade-offs, convergence under extreme data heterogeneity, and the integration of large language models for improved interpretability. Overall, FedMVC represents a promising framework for privacy-preserving unsupervised learning in distributed environments. Full article

Peer-to-peer (P2P) energy trading combines local energy resources, market coordination, data-driven decisions, and transaction management. This review examines how machine learning (ML) and blockchain are used across these functions and separates market and ledger processes from physical electricity delivery. A structured review procedure was applied to a corpus of 52 peer-reviewed journal articles, including the core P2P energy-trading evidence and a limited number of closely related contextual studies, supplemented by 10 non-journal or foundational sources, using defined search families, screening criteria, and qualitative synthesis. The literature is organized by the functional role of ML and compared across architecture, market operation, trust, consensus, privacy, and implementation. The consensus discussion considers practical Byzantine fault tolerance, Istanbul Byzantine fault tolerance, proof-of-authority, and application-oriented Byzantine-fault-tolerance variants, while the privacy discussion distinguishes federated learning, differential privacy, zero-knowledge proofs, and secure multiparty computation. Two deterministic MATLAB examples are included only for illustration. In the five-prosumer forecasting example, regression reduced mean absolute error (MAE) from 0.4240 to 0.2219 kWh and the hourly grid-import mismatch from 30.3529 to 7.9029 kWh. In the 10-peer workflow, five trades settled 7.7587 kWh, corresponding to 59.35% of the horizon-level surplus–deficit denominator defined in the simulation. These examples do not validate feeder feasibility, consensus performance, cryptographic security, or deployment readiness. Full article

Transformer architectures, originally developed for language modeling, have been increasingly applied to time-series forecasting, but deploying them on sensitive data such as patient vital signs, financial records, or energy use raises privacy risks at two points: inference (the server sees raw inputs and outputs) and training (the model can memorize individual records). This survey organizes privacy-preserving methods for Transformer forecasting along a two-axis taxonomy: protection stage (inference, training, and data-level) and protection locus/mechanism (cryptographic wrapper, gradient, input, feature, output, or data path). For inference, we review secure multi-party computation (CrypTen through MPCFormer, SecFormer, Iron, BOLT, PUMA, BumbleBee, and CipherPrune) and homomorphic encryption (CKKS, THE-X, hybrid HE-MPC, and automated FHE compilation via Orion), focusing on how each handles the Transformer non-linearities (softmax, GELU, and LayerNorm). For training, we organize differential-privacy mechanisms by where noise enters the gradient path (DP-SGD, DP-FedAvg, DP-FTRL, PATE, and adaptive clipping), the input path, and the more recent feature path and analyze the privacy, utility, and dimensionality trade-offs. We further survey privacy in large language and foundation models, federated Transformers, time-series privacy attacks (membership inference, gradient inversion, and attribute inference), differentially private synthetic data, and applications in energy, health care, and finance, closing with open problems and future directions. Throughout, we show that the surveyed mechanisms provide non-comparable privacy guarantees and should be chosen by threat model instead of headline utility. Full article

Differential privacy protection in federated learning faces the fundamental challenge of noise accumulation: noise added dispersedly by clients accumulates in variance during server-side aggregation, severely harming model convergence and accuracy. This paper proposes FedDecouple, a phase-decoupled differentially private federated learning framework that is analytically suited for resource-constrained mobile devices. The core innovation lies in decoupling the noise addition phase from the client computation phase—clients only upload clean gradients, while two auxiliary servers collaboratively generate and inject noise through a secure two-party MPC protocol. This design reduces the effective noise variance while eliminating the per-sample gradient computation burden on clients. Experimental results show that on MNIST, FedDecouple maintains 97.75% accuracy under strict privacy, significantly outperforming client-side noised DP-SGD with 94.0% accuracy. On CIFAR-10, it achieves 76.2% test accuracy, which is 13.4 percentage points higher than DP-SGD. FedDecouple’s total training time on both datasets is faster than Opacus and DP-SGD. Full article

Multi-server private information retrieval (PIR) based on function secret sharing (FSS) has emerged as a prominent paradigm for achieving sublinear communication. However, standard FSS constructions require full server participation, making them highly vulnerable to single-node fail-stop faults. Existing fault-tolerant schemes mitigate this but inevitably inflate the response overhead to scale with the database size N (e.g., $O\left(\sqrt{N}\right)$). To overcome this limitation, we propose a fault-tolerant PIR (FT-PIR) protocol based on a newly designed $(t,p)$-threshold distributed point function (FT-DPF). By introducing a hierarchical recursive patching mechanism, our scheme transforms rigid all-party evaluations into flexible t-out-of-p reconstructions. This architecture completely decouples the response communication from N and ensures efficient client-side reconstruction via lightweight XOR aggregations. Formal analysis proves that our stateless protocol guarantees $(t-1)$-computational privacy under the semi-honest model. Theoretical analysis demonstrates that the proposed FT-PIR achieves a response complexity bounded by $\mathcal{O}\left({\mathcal{F}}_{\mathrm{maxlevel}}(t,p)\right)$. Comprehensive experimental evaluations confirm that our implementation significantly reduces practical communication and computation overheads, outperforming the state-of-the-art scheme. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Computer networks constitute a critical component of contemporary digital infrastructure, and their secure and reliable operation is strongly dependent on the correctness, consistency, and accountability of the configurations of the network devices. Although traditional configuration management approaches remain widely adopted, they are primarily based on trust in centralized management systems, repositories, and privileged administrators, which may not fully guarantee tamper resistance or independent verification of configuration change histories. This paper examines the potential of blockchain technology as a mechanism for ensuring the integrity and accountability of configuration changes in network devices. To address this issue, a distributed four-layer architecture is proposed, comprising the network device layer, the configuration layer, the server layer, and the blockchain layer. Within this model, configuration changes are not applied permanently until they have passed a distributed approval procedure involving multi-party authorization, administrator voting, and digital signatures. The approved changes are subsequently recorded on the blockchain as immutable and cryptographically secured audit entries. The proposed concept was validated in a Python-based (version 3.7) simulation environment that reflects distributed management of routers and switches. The results indicate that the blockchain can provide more than a simple event logging mechanism. It can strengthen the security of the configuration approval process, reduce the risk of unauthorized modifications, improve resistance to tampering, and support distributed validation of data integrity. The proposed approach therefore offers a promising foundation for further research on scalable, secure, and auditable configuration management in network infrastructures. Full article

The surging integration of volatile renewable energy severely exacerbates power grid frequency fluctuations, yet conventional frequency regulation (FR) market clearing mechanisms fail to efficiently coordinate heterogeneous energy storage systems (ESSs) due to the complete decoupling of multi-dimensional physical performance from economic dispatch. To resolve this critical industry bottleneck, this paper proposes a novel Stackelberg game-based clearing mechanism tailored for diverse ESS participation. A bi-level optimization framework is constructed to internalize physical FR characteristics into market economics; the upper level minimizes the system operator’s total procurement costs by transforming multi-dimensional physical metrics—including dynamic response rates, time delays, and control accuracy—into endogenous performance penalty factors. Concurrently, the lower level maximizes the individual revenues of heterogeneous ESS aggregators under a Gini coefficient-based fairness constraint to mitigate profit monopolization and promote a more sustainable market ecology. To address the computational challenges of high-dimensional non-convexity, an enhanced hybrid Genetic Algorithm and Quadratic Programming (GA-QP) solver is developed to secure robust convergence to the Stackelberg equilibrium. Comprehensive simulation results confirm that the proposed Stackelberg game-based clearing mechanism enables a highly rational, quality-driven allocation of frequency regulation capacity. By dynamically linking physical performance metrics with economic benefit factors, it successfully achieves an optimal balance of interests between heterogeneous energy storage aggregators and the overarching market. Crucially, compared to conventional purely economic models, this mechanism structurally prevents absolute technology monopoly—drastically reducing the market Gini coefficient from a hazardous 0.85 to a healthy 0.32—while sustaining multi-party equity at a negligible system cost increase of only 1.64%. Ultimately, this framework offers a highly feasible and resilient solution for the efficient clearing of multi-type energy storage in modern power systems. Full article

The rapid growth of sensitive labeled data in healthcare, finance, user profiling, and commercial databases has created increasing demand for privacy-preserving collaborative data validation across different organizations. Following prior cryptographic studies, this paper uses private collaborative data cleaning (PCDC) to refer to a specific label-conflict detection task rather than general-purpose data cleaning: the goal is to identify records for which the identifiers match but the associated labels are inconsistent, without revealing the remaining private records. Existing PCDC protocols are mainly designed for direct two-party settings where data owners must remain online and participate in the main secure computation. To reduce this online burden, we propose SS-PCDC, a secret sharing-based PCDC framework in a cloud-assisted setting. Clients locally preprocess and secret-share their labeled datasets with two non-colluding cloud servers, which perform element matching, label consistency checking, and conflict detection over secret shares. Hash-based binning is used to reduce unnecessary secure comparisons. We instantiate the framework with two concrete protocols based on arithmetic secret sharing and Boolean secret sharing, respectively. We further extend exact PCDC to threshold-based fuzzy label conflict detection and propose SS-FPCDC, which reports a matched record as conflicting when the Hamming distance between its labels exceeds a public threshold. Security analyses show that the proposed protocols securely realize their corresponding ideal functionalities against a static semi-honest adversary corrupting at most one cloud server. Experimental results demonstrate the efficiency and scalability of SS-PCDC in its intended cloud-assisted setting, particularly for large datasets and longer labels. Full article

Homomorphic Encryption plays a key role in secure multi-source data aggregation because it enables computations to be performed directly over encrypted data, allowing distributed parties to contribute sensitive information while preserving confidentiality. However, its use in this context introduces three main challenges: existing approaches often focus on specific operations rather than supporting diverse analytics across multiple encrypted data sources; key generation and management frequently rely on trusted third parties or require private keys to be shared; and TEE-based solutions may avoid trusted third parties but often require computations to be partially executed inside the trusted environment, thereby limiting deployment flexibility. To address these limitations, this work makes three main contributions: (i) the proposal of a complete framework for secure multi-source data aggregation that leverages homomorphic encryption and enables any data consumer to securely run multi-source data aggregations over data previously registered by untrusted data providers; (ii) the integration of secure enclaves for the secure generation, distribution, and management of homomorphic keys, addressing challenges related to coordinated key synchronization in multi-party aggregation environments and removing the need for a trusted third party; and (iii) the introduction of a hybrid key management protocol that combines secure enclave-based key generation with efficient key distribution and secure aggregation outside the enclave, in the data consumer, minimizing trust assumptions and computational overhead. The implementation and evaluation on small-scale aggregated datasets show that the proposed approach effectively addresses the identified challenges by providing, to the best of the authors’ knowledge, the first practical and privacy-preserving solution that supports different algorithms without relying on any trusted third party, while improving over existing solutions through the integration of secure enclaves and a hybrid key management protocol. Full article

Artificial intelligence (AI) agents increasingly need credentials such as application programming interface (API) keys and Secure Shell (SSH) credentials, but placing those secrets in the agent process exposes them to prompt injection, tool misuse, and exfiltration through ordinary agent outputs. We present CapSeal, a capability-based broker that replaces direct secret access with session-bound, non-exportable handles. Agents request policy-evaluated actions, while the broker performs credential-bearing Hypertext Transfer Protocol (HTTP) and SSH execution through typed executors with schema validation, replay protection, revocation epochs, and tamper-evident audit logging. We extend this design to hosted settings where the broker operator is not trusted with tenant secrets. Our main contribution is operator-blind secret mediation: a split-broker architecture in which a small trusted tenant gateway cooperates with an untrusted operator service that stores the master secret only as a fully homomorphic encryption (FHE) ciphertext and evaluates per-request derivations without decrypting it. We formalize the model and prove computational operator blindness from indistinguishability under chosen-plaintext attack (IND-CPA) security of the FHE scheme, together with conditional capability binding for any secure pseudorandom function/message authentication code (PRF/MAC) instantiation. We implement an end-to-end TFHE-rs prototype that exercises split-broker derivation, multi-tenant revocation and rate limiting, audit integration, and HTTP/SSH mediation. The prototype uses a non-cryptographic homomorphic stand-in and measures the cost of crossing the operator-untrusted boundary at about 9 s per request, roughly 17 million times slower than the plaintext path. We also give LowMC and Rasta transciphering designs and compare FHE with trusted execution environment (TEE)- and secure multiparty computation (MPC)-based alternatives, positioning each trust boundary by assurance and performance. Full article

The quick transition of the healthcare industry to digital during the era of the Internet of Medical Things and Artificial Intelligence has ignited the demand for frameworks for data sharing while retaining safety and patient privacy. Centralized learning models place potentially sensitive patient data at risk of leakage, regulatory violation, and cyber-attacks which undermine receptivity and responsible ownership of big medical data. Federated learning is a novel paradigm that allows patients from various healthcare entities to train machine learning models while maintaining the ability to leverage their data without sharing their direct data. This study proposes a systematic literature review of approaches of privacy-preserving federated learning frameworks in healthcare applications. Following PRISMA guidelines, searches were conducted across Web of Science, Scopus, IEEE Xplore, ScienceDirect, PubMed, and ACM Digital Library with predefined query strings, explicit inclusion/exclusion criteria, and quality appraisal procedures. A total of 80 peer-reviewed studies, published from January 2015 to December 2025, were included in this systematic review, which examined cryptographic, architectural and algorithmic methods including differential privacy, homomorphic encryption, and Secure Multi-Party Computation, along with integrations using blockchain to enhance trust and confidence in distributed healthcare systems. The findings indicate a gradual shift towards hybrid privacy-preserving federated learning architectures which combined multiple security mechanisms to improve trust, confidentiality and robustness. Although significant progress has been achieved, the real-world deployment of such systems is heavily affected due to the challenges in communication efficiency, non-IID data distribution, adversarial attacks, and regulatory requirements. This research highlights future research directions for scalable, explainable and interoperable federated architectures that strike an optimal balance of privacy, utility and system performance for next-gen health intelligence. Trial registration: PROSPERO (CRD420261401073). Full article

The increasing need for privacy in machine learning has driven interest in cryptographic methods that enable computation on sensitive data. Secure multi-party computation and fully-homomorphic encryption are two prominent approaches, but their practical trade-offs are often unclear for real-world use. This work presents a pragmatic benchmark and comparison of these technologies for machine learning inference. Using representative open source frameworks, we evaluate performance across basic operations, distance metrics, regression models, and common deep learning architectures. The evaluation accounts for realistic deployment conditions, including communication overhead and partial plaintext execution. The results show that secure multi-party computation scales well to large and complex models but is strongly affected by network latency, while fully-homomorphic encryption is easier to deploy and performs well for small models and regression tasks. We conclude that the two approaches are complementary rather than competing and provide concrete guidance to help practitioners select the most suitable technology for privacy preserving machine learning in practice. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Quantum Secure Multi-party Summation (QSMS) is a fundamental primitive of Quantum Secure Multi-party Computation (QSMC), enabling multiple participants to jointly compute the sum of their private inputs without disclosing individual data. However, most existing QSMS protocols require all participants to possess full quantum capabilities and often rely on pre-shared keys, auxiliary mask transmission, or multiple trusted third parties, resulting in high communication overhead and limited practicality. To address these limitations, we propose an efficient Semi-Quantum Secure Multi-party Summation (SQSMS) protocol based on d-dimensional n-particle entangled states. By exploiting the global correlation properties of high-dimensional entangled states, the proposed protocol generates correlated random masks directly from quantum measurement outcomes. These masks cancel automatically during the aggregation process, eliminating the need for additional mask distribution and transmission. Compared with existing QSMS schemes, the proposed protocol reduces communication overhead, improves quantum efficiency, and avoids reliance on pre-shared keys or multiple trusted third parties. Moreover, only simple measurement operations are required from classical participants, making the protocol more practical for semi-quantum environments. We further provide formal correctness and security analyses of the proposed protocol and conduct quantum circuit simulations using the IBM Qiskit platform to demonstrate its feasibility. Moreover, based on the proposed summation protocol, we design several extended application protocols, including anonymous voting, anonymous auction, and anonymous ranking, which further illustrate the scalability and practical applicability of the proposed scheme. Full article