ShieldIR: Privacy-Preserving Unsupervised Cross-Domain Image Retrieval via Dual Protection Transformation

摘要

Unsupervised cross-domain image retrieval (UCDIR) aims to retrieve images across different domains without the guidance of labels. However, existing UCDIR methods assume that data can be shared across domains in plaintext, which is often impractical due to strict data privacy protection policies. In this paper, we propose ShieldIR, a novel privacy-preserving unsupervised cross-domain image retrieval framework that enhances the retrieval performance across two domains while safeguarding data privacy. ShieldIR unifies intra-domain and cross-domain representation learning through a Dual Protection Transformation (DPT) module, which introduces a structured feature space via orthogonal projection and ensures data privacy by adding calibrated differential privacy noise. This transformation allows ShieldIR to preserve semantic structure while formally protecting private data. For intra-domain representation learning, ShieldIR enhances discriminability by using DPT to map prototypes into an independent feature space and subsequently aligning the resulting dual-protected prototypes with instance-level features. For cross-domain alignment, ShieldIR maps intra-domain features and cross-domain prototypes into a shared structured space using DPT, achieving semantic alignment under privacy constraints. Extensive experiments on real-world datasets demonstrate that our ShieldIR outperforms state-of-the-art methods while effectively protecting data privacy.