Authors:
Chaitanya Bharath Somineni

Addresses:
Departement of Data Engineering, Toyota Motor Credit Corporation, Plano, Texas, United States of America.

Abstract:

In autonomous data exchange environments, where data is flowing in real-time, it is crucial to have comprehensive security solutions to ensure user privacy and system performance. This paper presents a Dynamic Privacy Preservation Architecture specifically developed for untrusted, decentralised networks of autonomous nodes that frequently interact. The framework uses adaptive anonymisation algorithms and decentralised trust verification mechanisms to dynamically protect private data tokens based on context sensitivity and the recipient's risk profile. The evaluation of this architecture was conducted through an experimental study using a synthetic dataset from the operational Internet of Things network, comprising 159 distinct communications. Using the Python programming language and Python-specific data science packages such as Pandas for data manipulation, Scikit-learn for metric evaluation, and Matplotlib for visual plotting, system performance, computational overhead and privacy metrics were simulated and analysed. The results show that the proposed architecture is suitable to optimise the data utility-privacy trade-off in absolute terms while preserving strict privacy. The system dynamically scales its defence mechanisms to keep processing latencies low and effectively prevent unauthorised reconstruction attacks, thereby providing a reliable, secure and highly scalable ecosystem for autonomous data stakeholders.

Keywords: Privacy Preservation; Autonomous Ecosystems; Data Exchange; Trust Architecture; Adaptive Anonymisation; Dynamic Privacy Preservation; Adaptive Anonymisation Algorithms.

Received on: 08/07/2025, Revised on: 01/10/2025, Accepted on: 12/10/2025, Published on: 09/08/2026

DOI: 10.69888/FTSCL.2026.000750

FMDB Transactions on Sustainable Computer Letters, 2026 Vol. 4 No. 3, Pages: 160-168

• Views : 13
• Downloads : 7

Download PDF