Privacy-Preserving Outsourcing of Medical Image Data using SIFT Descriptor
Keywords:
Image matching, SIFT, DoGAbstract
Outsourcing huge amount of personal multimedia data in these days become a challenging task for the data owners which is greatly motivated by the advances in cloud computing by using its several resources for cost saving and flexibility. despite these facts, outsourcing of multimedia data may leak the data owner’s private information, such as the personal identity, locations, or even financial profiles.in this paper, we present an effective and practical privacy-preserving computation outsourcing protocol for persuading scale-invariant feature transform (SIFT) over huge encrypted image data. We first explain the previous solutions to this problem which is either efficiency or security issues, and no one can well maintain the important functionality of the original SIFT in terms of distinctiveness and robustness. Next, we present a new scheme that achieves practicality requirements along with the maintenance of its key functionality, by first splitting the original image data and designing two novel efficient protocols for secure calculations like multiplication and comparison, then carefully distributing the feature extracted onto two independent cloud servers. Which results into practically secure solution and outperforms the state-of-the-art, with the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, and an addition of noise and change in 3D viewpoint and illumination. To deal with the privacy of important medical multimedia data we took brain tumor as our case study. The Brain Tumor is affecting many people worldwide. It is not only limited to the old age people but also detected in the early age. The encrypted images are stored in the cloud. From the encrypted images we will check for brain tumor using OpenCV and preserve this information by getting revealed using our proposed method.
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