A User-Centric Context-Aware Framework for Real-Time Optimisation of Multimedia Data Privacy Protection, and Information Retention Within Multimodal AI Systems

The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability to users’ preferences and contextual requirements,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 19; p. 6105
Main Authors: Topalli, Ndricim, Badii, Atta
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 03.10.2025
Subjects:
Algorithms
Artificial Intelligence
Biometrics
Biometry
Compliance
Computer Security
Data integrity
data intelligibility
Decision making
Facial recognition technology
hard biometrics
Humans
Machine learning
Multimedia
multimodal data
Ontology
Privacy
privacy engineering
Privacy, Right of
re-identification
Semantics
soft biometrics
Usability
User behavior
privacy protection
dynamic privacy adaptation
privacy engineering
data intelligibility
real-time data obfuscation
re-identification
user-centric privacy
context-aware AI
hard biometrics
multimodal data
GDPR compliance
soft biometrics
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability to users’ preferences and contextual requirements, and obfuscate user faces uniformly. This research proposes a user-centric, context-aware, and ontology-driven privacy protection framework that dynamically adjusts privacy decisions based on user-defined preferences, entity sensitivity, and contextual information. The framework integrates state-of-the-art recognition models for recognising faces, objects, scenes, actions, and emotions in real time on data acquired from vision sensors (e.g., cameras). Privacy decisions are directed by a contextual ontology based in Contextual Integrity theory, which classifies entities into private, semi-private, or public categories. Adaptive privacy levels are enforced through obfuscation techniques and a multi-level privacy model that supports user-defined red lines (e.g., “always hide logos”). The framework also proposes a Re-Identifiability Index (RII) using soft biometric features such as gait, hairstyle, clothing, skin tone, age, and gender, to mitigate identity leakage and to support fallback protection when face recognition fails. The experimental evaluation relied on sensor-captured datasets, which replicate real-world image sensors such as surveillance cameras. User studies confirmed that the framework was effective, with over 85.2% of participants rating the obfuscation operations as highly effective, and the other 14.8% stating that obfuscation was adequately effective. Amongst these, 71.4% considered the balance between privacy protection and usability very satisfactory and 28% found it satisfactory. GPU acceleration was deployed to enable real-time performance of these models by reducing frame processing time from 1200 ms (CPU) to 198 ms. This ontology-driven framework employs user-defined red lines, contextual reasoning, and dual metrics (RII/IVI) to dynamically balance privacy protection with scene intelligibility. Unlike current anonymisation methods, the framework provides a real-time, user-centric, and GDPR-compliant method that operationalises privacy-by-design while preserving scene intelligibility. These features make the framework appropriate to a variety of real-world applications including healthcare, surveillance, and social media.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25196105