The increasing use of Online Vision Language Models (OVLMs) for processing images has introduced significant privacy risks, as individuals frequently upload images for various utilities, unaware of the potential for privacy violations. Images contain relationships that relate to Personally Identifiable Information (PII), where even seemingly harmless details can indirectly reveal sensitive information through surrounding clues. This paper explores the critical issue of PII disclosure in images uploaded to OVLMs and its implications for user privacy. We investigate how the extraction of contextual relationships from images can lead to direct (explicit) or indirect (implicit) exposure of PII, significantly compromising personal privacy. Furthermore, we propose methods to protect privacy while preserving the intended utility of the images in Vision Language Model (VLM)-based applications. Our evaluation demonstrates the efficacy of these techniques, highlighting the delicate balance between maintaining utility and protecting privacy in online image processing environments. Index Terms-Personally Identifiable Information (PII), Privacy, Utility, privacy concerns, sensitive information
Figures & tables
Fig. 1 : PPA system
Fig. 2 : Object Detection Module
Fig. 3 : Analysis component
Fig. 4 : Responses to “Where is this image located?” show how obfuscation alters location-specific PII inference.
Fig. 5 : Impact of privacy-preserving techniques on privacy gain, utility impact and VLM outputs.
Privacy protection in vision-language models limits sensitive information exposure while preserving useful image understanding and multimodal reasoning.