Privacy-Preserving Medical Image Classification through Deep Learning
  and Matrix Decomposition
Andreea Bianca Popescu Cosmin Ioan Nita Ioana Antonia Taca Anamaria Vizitiu Lucian Mihai Itu
Deep learning (DL)-based solutions have been extensively researched in the
medical domain in recent years, enhancing the efficacy of diagnosis, planning,
and treatment. Since the usage of health-related data is strictly regulated,
processing medical records outside the hospital environment for developing and
using DL models demands robust data protection measures. At the same time, it
can be challenging to guarantee that a DL solution delivers a minimum level of
performance when being trained on secured data, without being specifically
designed for the given task. Our approach uses singular value decomposition
(SVD) and principal component analysis (PCA) to obfuscate the medical images
before employing them in the DL analysis. The capability of DL algorithms to
extract relevant information from secured data is assessed on a task of
angiographic view classification based on obfuscated frames. The security level
is probed by simulated artificial intelligence (AI)-based reconstruction
attacks, considering two threat actors with different prior knowledge of the
targeted data. The degree of privacy is quantitatively measured using
similarity indices. Although a trade-off between privacy and accuracy should be
considered, the proposed technique allows for training the angiographic view
classifier exclusively on secured data with satisfactory performance and with
no computational overhead, model adaptation, or hyperparameter tuning. While
the obfuscated medical image content is well protected against human
perception, the hypothetical reconstruction attack proved that it is also
difficult to recover the complete information of the original frames.