SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models

Jiang Zhang, Rohan Xavier Sequeira, Konstantinos Psounis

Keywords: machine learning, synthetic data, privacy, utility

Abstract:

Specialized machine learning (ML) models tailored to users’ needs
and requests are increasingly being deployed on smart devices
with cameras, to provide personalized intelligent services taking
advantage of camera data. However, two primary challenges hinder
the training of such models: the lack of publicly available labeled
data suitable for specialized tasks and the inaccessibility of labeled
private data due to concerns about user privacy. To address these
challenges, we propose a novel system SpinML, where the server
generates customized Synthetic image data to Privately traIN a
specialized ML model tailored to the user request, with the usage of
only a few sanitized reference images from the user. SpinML offers
users fine-grained, object-level control over the reference images,
which allows users to trade between the privacy and utility of the
generated synthetic data according to their privacy preferences.
Through experiments on three specialized model training tasks,
we demonstrate that our proposed system can enhance the performance
of specialized models without compromising users’ privacy
preferences.

Submission Number: 31