A Differentially Private Collaborative Intelligence Structure for Trust Aware Secure Data Exchange in Distributed Computing Platforms

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.