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Privacy-Preserving Image Template Sharing Using Contrastive Learning
2022
Entropy
With the recent developments of Machine Learning as a Service (MLaaS), various privacy concerns have been raised. Having access to the user's data, an adversary can design attacks with different objectives, namely, reconstruction or attribute inference attacks. In this paper, we propose two different training frameworks for an image classification task while preserving user data privacy against the two aforementioned attacks. In both frameworks, an encoder is trained with contrastive loss,
doi:10.3390/e24050643
pmid:35626528
pmcid:PMC9141880
fatcat:z6svnfowzbe3zas4jftbpzy46m