Non-bijectivity-based image obfuscation method for deep learning based medical applications

As more and more deep learning (DL) solutions are employed in the healthcare domain using the Machine Learning as a Service (MLaaS) paradigm, concerns regarding personal data privacy have been raised. In this context, especially in medical imaging, the demand for privacy-preserving techniques, that allow for DL model development, has recently increased significantly. Herein, we propose a medical image obfuscation algorithm based on pixel intensity shuffling and non-bijective functions. The proposed algorithm is evaluated on a medical use case based on coronary angiographic images. Multiple convolutional neural networks are trained to measure the utility of the obfuscated images. An attack configuration based on artificial intelligence (AI) is evaluated to validate the level of privacy. The classification performance on the obfuscated images is satisfactory, while the computational time is not affected significantly. Visual and metrics-based analyses show that the data is protected from human perception and from AI-based image reconstruction approaches.