Authors:
P. Mohaideen Fathima, V. Sabaresan, R. Vanitha, C. Gayathri, M. Briskilla, Arno Onnen, Ijaz Ahad, Rahul Panakkal

Addresses:
Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, St Joseph's Institute of Technology, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, KCG College of Technology, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu, India. School of Computer Science and Information Technology, University of Edinberg, Lusaka, Zambia. Department of Information Sciences, University of Library Studies and Information Technologies, Tsarigradsko Shose, Sofia, Bulgaria. Department of Information Sciences, Duale Hochschule Baden-Württemberg, Stuttgart, Baden-Württemberg, Germany. Department of Computer Systems Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Department of Computer Systems Engineering, University of Engineering and Applied Sciences, Swat, Khyber Pakhtunkhwa, Pakistan. Department of Computer Science and Engineering, University of Illinois at Urbana, Champaign, Illinois, United States of America.

Abstract:

Human-centric digital intelligence ecosystems need to have context-aware privacy controls that do not impair effective personalization and are aware of consent, identity sensitivity and trust expectations. This paper introduces the Cognitive Privacy Shielding Model, which aims to secure digital environments where people interact with intelligent services, including learning platforms, enterprise portals, smart health interfaces and connected decision support systems. The research is based on an author-generated dataset of 117 instances of consent events, identity exposure signals, behavioral traces, risk alerts, access decisions and trust responses. Data were prepared and graphs generated using Python; tabular structuring was done using MS Excel, and the manuscript was organized using MS WORD. The model introduces a privacy fence between human signals and digital intelligence systems, enabling privacy masking, consent checking, risk monitoring, policy feedback and trusted output delivery. The results show that layered shielding enhances control, supports more sensitive attributes, boosts user confidence, reduces unmanaged exposure, and promotes intelligence-driven interactions. The main novelty is that all four elements - cognitive awareness, privacy gating, adaptive response, and governance monitoring - have been integrated into a compact architecture optimized for human-centered platforms. Overall, the paper presents a design path for implementing secure digital ecosystems that support services and respect individual autonomy.

Keywords: Human-Centric AI; Cognitive Privacy; Privacy Controls; Consent Management; Trust-Aware Systems; Risk Monitoring; Adaptive Response; Digital Ecosystems; Intelligent Services.

Received on: 28/09/2025, Revised on: 19/11/2025, Accepted on: 16/01/2026, Published on: 03/06/2026

DOI: 10.69888/FTSCIS.2026.000764

FMDB Transactions on Sustainable Critical Infrastructures, 2026 Vol. 1 No. 2, Pages: 89-97

• Views : 48
• Downloads : 11

Download PDF