Compressive Sensing of Medical Images With Confidentially Homomorphic Aggregations

Abstract

Efficient medical image sampling and transferring becomes one of key research areas in computer science and healthcare application industries. In particular, the technique of body area networking and personal area networking are very useful in various image-based medical monitoring systems that cover a wide range of healthcare services, such as early detecting of emergency conditions and remote online instructing of surgeries. However, medical images are highly privacy sensitive and redundant. Thus, proper protection on privacy and secure data aggregation/compression are also highly expected in medical image processing. Based on compressive sensing theory, we conceive a so-called “one-stone-three-bird” solution for medical image acquisition and transmission in this paper. The size of the original medical images can be reduced to 20%, the resulted images have very well confidentiality and supporting additively homomorphic aggregation.