Elaris Computing Nexus
A Differentially Private Collaborative Intelligence Structure for Trust Aware Secure Data Exchange in Distributed Computing Platforms
Elaris Computing Nexus
Received On : 06 May 2026
Revised On : 12 June 2026
Accepted On : 26 June 2026
Published On : 08 July 2026
Volume 02, 2026
Pages : 109-121
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.
Keywords
Differential Privacy, Trust-Aware Computing, Secure Data Exchange, Collaborative Intelligence, Distributed Computing Platforms.
- S. P. Gurusamy, S. Sharma, and A. K. V, "Integrate blockchain and federated learning approaches to secure multi-cloud data exchange: An AI-powered trust management system," in Proc. 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Sep. 2025, pp. 1–6, doi: 10.1109/ICERECT65215.2025.11378085.
- I.-H. Chuang, S.-H. Huang, W.-C. Chao, J.-S. Tsai, and Y.-H. Kuo, "TIDES: A trust-aware IoT data economic system with blockchain-enabled multi-access edge computing," IEEE Access, vol. 8, pp. 85839–85855, 2020, doi: 10.1109/ACCESS.2020.2991267.
- Z. Hameed, C. Pahl, M. H. Berenjestanaki, and I. Van Thillo, "A secure and reliable distributed storage system with multi-factor trust-aware workflow scheduling," Cluster Computing, vol. 28, no. 16, Oct. 2025, doi: 10.1007/s10586-025-05625-1.
- M. Mrabet, "TrustFed-CTI: A trust-aware federated learning framework for privacy-preserving cyber threat intelligence sharing across distributed organizations," Future Internet, vol. 17, no. 11, p. 512, Nov. 2025, doi: 10.3390/fi17110512.
- P. K. Samant, V. Pathak, W. Ahmad, and A. Alabdultif, "A lightweight trusted framework for secure data exchange and threat mitigation in IoT-enabled healthcare environments," Scientific Reports, vol. 15, no. 1, Nov. 2025, doi: 10.1038/s41598-025-22797-3.
- X. Li, H. Ma, W. Yao, and X. Gui, "Data-driven and feedback-enhanced trust computing pattern for large-scale multi-cloud collaborative services," IEEE Transactions on Services Computing, vol. 11, no. 4, pp. 671–684, Jul. 2018, doi: 10.1109/TSC.2015.2475743.
- J. Xu, C. Zhang, L. Jin, and C. Su, "A trust-aware incentive mechanism for federated learning with heterogeneous clients in edge computing," Journal of Cybersecurity and Privacy, vol. 5, no. 3, p. 37, Jun. 2025, doi: 10.3390/jcp5030037.
- B. Ali, M. A. Gregory, and S. Li, "Trust-aware task load balancing in multi-access edge computing based on blockchain and a zero trust security capability framework," Transactions on Emerging Telecommunications Technologies, vol. 34, no. 12, Aug. 2023, doi: 10.1002/ett.4845.
- B. Ali, M. A. Gregory, S. Li, and O. A. Dib, "Implementing zero trust security with dual fuzzy methodology for trust-aware authentication and task offloading in multi-access edge computing," Computer Networks, vol. 241, p. 110197, Mar. 2024, doi: 10.1016/j.comnet.2024.110197.
- W. Almuseelem, "Secure latency-aware task offloading using federated learning and zero trust in edge computing for IoMT," IEEE Access, vol. 13, pp. 117808–117830, 2025, doi: 10.1109/ACCESS.2025.3586730.
- M. Attar and W. Lucia, "A data-driven safety preserving control architecture for constrained cyber-physical systems," International Journal of Robust and Nonlinear Control, vol. 35, no. 1, pp. 343–358, Oct. 2024, doi: 10.1002/rnc.7654.
- S. Wang and T. Ma, "A two-way trust-based routing approach to identify malicious and energy-aware nodes in fog computing," Cluster Computing, vol. 28, no. 7, Jul. 2025, doi: 10.1007/s10586-025-05254-8.
- K. Hayawi, I. Makhdoom, S. Khalid, R. A. Ikuesan, M. Kaosar, and I. Ahmad, "A false positive resilient distributed trust management framework for collaborative intrusion detection systems," IEEE Transactions on Services Computing, vol. 18, no. 2, pp. 513–526, Mar. 2025, doi: 10.1109/TSC.2025.3539202.
- U. R. Saxena, R. Kadel, Y. Khatri, A. Sharma, S. Shailendra, and M. T. Islam, "Trust aware cloud service transactions using blockchain and zero-knowledge proofs," International Journal of System Assurance Engineering and Management, Jun. 2026, doi: 10.1007/s13198-026-03382-x.
- J. De La Croix Kiguigouléli Ki and B. Zerbo, "Modeling dynamic trust for cooperative operations in edge computing environments," in Proc. IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD), Nov. 2025, pp. 1–8, doi: 10.1109/MNE3SD67637.2025.11323241.
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Cite this Article
Minlie Huang, “A Differentially Private Collaborative Intelligence Structure for Trust Aware Secure Data Exchange in Distributed Computing Platforms”, Elaris Computing Nexus, pp. 109-121, 2026, doi: 10.65148/ECN/2026009.
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© 2026 Minlie Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.