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DP-Image: Differential Privacy for Image Data in Feature Space

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arxiv 2103.07073 v2 pith:MD57IZJT submitted 2021-03-12 cs.CR cs.CV

Hanyu Xue , Bo Liu , Ming Ding , Tianqing Zhu , Dayong Ye , Li Song , Wanlei Zhou This is my paper · ORCID
classification cs.CRcs.CV
keywords privacyimagesdp-imagedifferentialfeatureimagedataspace
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The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even though differential privacy (DP) is a widely accepted criterion that can provide a provable privacy guarantee, the application of DP on unstructured data such as images is not trivial due to the lack of a clear qualification on the meaningful difference between any two images. In this paper, for the first time, we introduce a novel notion of image-aware differential privacy, referred to as DP-image, that can protect user's personal information in images, from both human and AI adversaries. The DP-Image definition is formulated as an extended version of traditional differential privacy, considering the distance measurements between feature space vectors of images. Then we propose a mechanism to achieve DP-Image by adding noise to an image feature vector. Finally, we conduct experiments with a case study on face image privacy. Our results show that the proposed DP-Image method provides excellent DP protection on images, with a controllable distortion to faces.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PASS: Private Attributes Protection with Stochastic Data Substitution

    cs.LG 2025-06 conditional novelty 7.0 of 10

    PASS learns a stochastic substitution mapping that drives private-attribute inference to chance level across images, audio, and sensor data while keeping useful attributes mostly intact.

  2. End-to-End Differential Privacy in Training Deep Neural Network Classifiers

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Perturbing softmax outputs with the Dirichlet mechanism during training yields input-private, label-public classifiers that beat prior differentially private training accuracy on five image benchmarks.

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