An Improved method for sharing medical images for Privacy Preserving Machine Learning using Multiparty Computation and Steganography

Abstract

Digital data privacy is one of the main concerns in today’s world. When everything is digitized, there is a threat of private data being misused. Privacy-preserving machine learning is becoming a top research area. For machines to learn, massive data is needed and when it comes to sensitive data, privacy issues arise.With this paper, we combine secure multiparty computation and steganography helping machine learning researchers to make use of a huge volume of medical images with hospitals without compromising patients’ privacy. This also has application in digital image authentication. Steganography is one way of securing digital image data by secretly embedding the data in the image without creating visually perceptible changes. Secret sharing schemes have gained popularity in the last few years and research has been done on numerous aspects.