A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications

Matteo Pennisi*, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ulas Bagci, Concetto Spampinato

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GANs as a proxy for data sharing is not a safe solution, as they tend to embed near-duplicates of real samples in the latent space. Recent works, inspired by k-anonymity principles, address this issue through sample aggregation in the latent space, with the drawback of reducing the dataset by a factor of k. Our work aims to mitigate this problem by proposing a latent space navigation strategy able to generate diverse synthetic samples that may support effective training of deep models, while addressing privacy concerns in a principled way. Our approach leverages an auxiliary identity classifier as a guide to non-linearly walk between points in the latent space, minimizing the risk of collision with near-duplicates of real samples. We empirically demonstrate that, given any random pair of points in the latent space, our walking strategy is safer than linear interpolation. We then test our path-finding strategy combined to k-same methods and demonstrate, on two benchmarks for tuberculosis and diabetic retinopathy classification, that training a model using samples generated by our approach mitigate drops in performance, while keeping privacy preservation. Code is available at: https://github.com/perceivelab/PLAN

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Pages422-431
Number of pages10
ISBN (Print)9783031438974
DOIs
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: Oct 8 2023Oct 12 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14222 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/TerritoryCanada
CityVancouver
Period10/8/2310/12/23

Keywords

  • generative models
  • latent navigation
  • privacy-preserving

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications'. Together they form a unique fingerprint.

Cite this