RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
This addresses privacy concerns for smart city and autonomous vehicle networks, offering a scalable solution, though it appears incremental as it builds on existing text-generation and privacy methods.
The paper tackles the conflict between visual data utility and privacy in Intelligent Transportation Systems by proposing RL-MoE, a framework that transforms images into privacy-preserving textual descriptions, reducing replay attack success to 9.4% on the CFP-FP dataset while generating richer text than baselines.
The proliferation of AI-powered cameras in Intelligent Transportation Systems (ITS) creates a severe conflict between the need for rich visual data and the right to privacy. Existing privacy-preserving methods, such as blurring or encryption, are often insufficient due to creating an undesirable trade-off where either privacy is compromised against advanced reconstruction attacks or data utility is critically degraded. To resolve this challenge, we propose RL-MoE, a novel framework that transforms sensitive visual data into privacy-preserving textual descriptions, eliminating the need for direct image transmission. RL-MoE uniquely combines a Mixture-of-Experts (MoE) architecture for nuanced, multi-aspect scene decomposition with a Reinforcement Learning (RL) agent that optimizes the generated text for a dual objective of semantic accuracy and privacy preservation. Extensive experiments demonstrate that RL-MoE provides superior privacy protection, reducing the success rate of replay attacks to just 9.4\% on the CFP-FP dataset, while simultaneously generating richer textual content than baseline methods. Our work provides a practical and scalable solution for building trustworthy AI systems in privacy-sensitive domains, paving the way for more secure smart city and autonomous vehicle networks.