[2603.03412] PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing

Computer Science > Cryptography and Security arXiv:2603.03412 (cs) [Submitted on 3 Mar 2026] Title:PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing Authors:Dipesh Tamboli, Vineet Punyamoorty, Atharv Pawar, Vaneet Aggarwal View a PDF of the paper titled PRIVATEEDIT: A Privacy-Preserving Pipeline for Face-Centric Generative Image Editing, by Dipesh Tamboli and Vineet Punyamoorty and Atharv Pawar and Vaneet Aggarwal View PDF HTML (experimental) Abstract:Recent advances in generative image editing have enabled transformative applications, from professional head shot generation to avatar stylization. However, these systems often require uploading high-fidelity facial images to third-party models, raising concerns around biometric privacy, data misuse, and user consent. We propose a privacy-preserving pipeline that supports high-quality editing while keeping users in control over their biometric data in face-centric use cases. Our approach separates identity-sensitive regions from editable image context using on-device segmentation and masking, enabling secure, user-controlled editing without modifying third-party generative models. Unlike traditional cloud-based tools, PRIVATEEDIT enforces privacy by default: biometric data is never exposed or transmitted. This design requires no access to or retraining of third-party models, making it compatible with a wide range of commercial APIs. By treating privacy as a core design constraint, our system...