The proposed framework combines adaptive differential privacy optimization and the trust-aware collaboration management to enable secure and reliable information sharing and achieves better privacy protection, less communication overhead and better collaborative intelligence performance than traditional privacy-aware exchange mechanisms.
Abstract
Distributed computing platforms are seeing a rapid increase in size and complexity, allowing multiple entities to share knowledge and gain computational insights as a group. Nevertheless, secure sharing of data in such settings is difficult because of problems with privacy leakage, untrusted parties, communication vulnerabilities and so on. Most existing privacy preserving techniques are primarily based on data perturbation techniques and do not consider the changing trust relationships between the collaborating nodes that affect the utility of the privacy ensuring technique and the efficient utilization of the resources. This paper presents a Differentially Private Collaborative Intelligence Framework for Trust-Aware Secure Data Exchange in Distributed Computing Platforms called PriviGuard. The proposed framework combines adaptive differential privacy optimization and the trust-aware collaboration management to enable secure and reliable information sharing. PriviGuard's multi-layer security shield architecture includes a trust evaluation, privacy calibration, secure aggregation, and collaborative intelligence layer. A trust-driven privacy optimization model tailors the amount of noise injected into the data depending on its reliability of the participant while preserving data utility. The framework allows for sharing knowledge securely across dispersed entities without releasing sensitive knowledge. Experimental tests show that PriviGuard achieves better privacy protection, less communication overhead and better collaborative intelligence performance than traditional privacy-aware exchange mechanisms. The suggested approach is a promising solution for secure, scalable, and adaptive collaboration in a distributed computing environment.
A privacy-preserving, secure data-sharing framework tailored for edge-cloud collaborative architectures that minimizes the computational overhead on the terminal side while safeguarding user privacy, and effectively reduces the overhead associated with user joining and revocation within the same group.
Qikun Zhang, Zheng Cai, Jinbo Feng et al.· Journal of King Saud Univers...· 0 citations
Cloud brokers play a vital role in coordinating resource-allocation decisions across a heterogeneous cloud environment. Centralised brokerage approaches are prone to single points of failure and data exposure, expose sensitive data, and have limited interoperability. There is a need for distributed cloud brokerage that enhances collaboration by enabling decision-making without exposing raw workload specifications. A shared knowledge repository in collaborative learning introduces data sovereignty and adversarial risks. This paper proposes Federated learning-based SecureBroker-FL, introducing 3 key aspects: (i) A Differential Privacy enhanced Gradient Encryption (DPGE) protocol. (ii) An Adaptive Trust scoring (ATS) mechanism, (iii) Hierarchical Secure Aggregation (HSA). The experiments are simulated and evaluated using the Google Cloud Trace 2019 and Alibaba Clustered Trace 2022 datasets, with five geographically distributed cloud brokers. It demonstrates that SecureBroker-FL achieves 94.7% decision accuracy. The framework withstands up to 40% malicious broker participation handling graceful degradation without catastrophic collapse and outperforms baseline approaches, including FedAvg, Krum, and FLTrust.
Chaitra M· Journal of Intelligent Decis...· 0 citations
Collaborative threat intelligence sharing has become essential for defending distributed enterprise and smart city infrastructures against increasingly sophisticated cyber threats. However, organizations remain reluctant to share raw security data due to privacy, regulatory, and trust concerns. Federated Learning (FL) has emerged as a promising solution by enabling collaborative model training without exposing local data. Nevertheless, traditional FL-based intrusion detection frameworks remain vulnerable to model poisoning, Byzantine attacks, and lack transparent accountability mechanisms for cross-organization collaboration. This paper proposes an engineering framework for privacy-preserving federated threat intelligence sharing that integrates trust-aware robust aggregation with blockchain-based integrity anchoring. The proposed architecture introduces a dynamic reputation mechanism that evaluates participant reliability across communication rounds and assigns adaptive aggregation weights to mitigate malicious updates. To enhance transparency and non-repudiation, model update hashes and trust evolution records are anchored on a permissioned blockchain through smart contracts, ensuring immutable auditability without exposing sensitive parameters. The framework is evaluated using a non-IID partition of the ToN-IoT dataset across multiple simulated organizations. Experimental results demonstrate significant robustness improvements under adversarial environments. Under Byzantine attacks with 20% malicious clients, the proposed trust-aware aggregation mechanism achieves approximately 95% Accuracy and 95% F1-macro, compared with approximately 89% obtained using conventional FedAvg. Furthermore, under highly adversarial conditions involving 40% malicious participants, the proposed framework maintains approximately 95% Accuracy and F1-macro, whereas FedAvg degrades to approximately 78% Accuracy and 77% F1-macro. These results confirm the effectiveness of the proposed trust-aware aggregation mechanism in mitigating malicious updates while preserving stable convergence and reliable intrusion detection performance.
Mehdi Houichi, Faouzi Jaidi, Adel Bouhoula· Journal of King Saud Univers...· 0 citations
In today’s era of big data, personal privacy is increasingly at risk due to widespread data sharing. Mobile applications often collect excessive personal information, while advanced analytics can sometimes lead to biased or discriminatory practices. These challenges create an urgent need for secure, privacy-preserving methods that allow sensitive data to be shared and analyzed across multiple parties and diverse systems. This paper reviews the progress made in this area, with a particular focus on the requirements for safe data sharing and controlled dissemination of private information during multi-party data fusion. The review is structured around three main perspectives: privacy-preserving computation, information sharing control, and collaborative secure computation. We begin by examining the current state of privacy protection in large-scale, interconnected environments, followed by a comparison of recent research developments at both national and international levels. In the area of privacy-preserving computation, emerging techniques such as full-lifecycle privacy safeguards, information flow control, and secure data exchange mechanisms are discussed. For information sharing control, three approaches are analyzed—local control, extended control, and desensitization methods. In collaborative secure computation, we outline methods currently being applied in both academic and industry contexts. Finally, the paper highlights key challenges and directions for future research. Traditional approaches such as anonymization, perturbation, and access control, as well as more advanced methods like cryptography and federated learning, all face practical limitations. To achieve robust protection throughout the entire data lifecycle, theoretical models and privacy-aware information systems must be further refined and tailored to different real-world application scenarios.
Chaitanya Tumma, Supraja Ayyamgari, Charan Thumma et al.· 2026 International Conferenc...· 0 citations
Edge computing and Internet of Things (IoT) have expanded the attack surface of modern networks. Security designs often tradeoff detection quality and privacy: centralized trust creates single points of failure, while distributed approaches may sacrifice accuracy or formal privacy guarantees. This article presents a federated trust modeling framework that integrates multimodal anomaly detection, Byzantine-resilient federated learning with $(\epsilon,\delta)$-differential privacy (DP), and context-aware zero trust architecture decision-making. The three-layer architecture comprises device-level trust learning with enhanced variational autoencoder, Isolation Forest, long short-term memory, and statistical process control modalities; a federated aggregation layer for robust aggregation with DP; and a trust scoring and decision layer that maps evidence to continuous trust and access levels using a subjective logic-inspired formulation. We provide detailed algorithmic implementations with pseudocode for each layer. Comprehensive evaluation demonstrates exceptional performance: high precision with low false positive rate, near-linear scalability achieving high efficiency, high accuracy with precision detecting most of attacks with zero false alarms, and sublinear time complexity. Privacy preservation is maintained through DP guarantees without accuracy degradation. The results support deployability studies for large-scale IoT and Artificial Intelligence of Things settings, with generalization to real telemetry left to future work.
Shengjie Xu, Yi Qian· IEEE Journal of Selected Are...· 0 citations
The findings indicate that unifying adaptive privacy preservation with decentralized integrity auditing yields a more complete cloud-security posture than either mechanism alone, and the paper outlines the empirical validation, including full-scale testbed experiments, required before deployment.
Jayakumar D, M. Ramamoorthy· International journal of com...· 0 citations
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