2DCrypt Image Scaling and Cropping in Encrypted Domains - 2016 PROJECT TITLE: 2DCrypt Image Scaling and Cropping in Encrypted Domains - 2016 ABSTRACT: The evolution of Cloud Computing and a drastic increase in image size are creating the outsourcing of image storage and processing an attractive business model. Although this outsourcing has many benefits, ensuring knowledge confidentiality within the cloud is one among the most concerns. There are state-of-the-art encryption schemes for making certain confidentiality within the cloud. However, such schemes do not permit cloud datacenters to perform operations over encrypted images. In this paper, we have a tendency to address this concern by proposing 2DCrypt, a changed Paillier cryptosystem-based image scaling and cropping theme for multi-user settings that allows cloud datacenters to scale and crop an image within the encrypted domain. To anticipate a high storage overhead resulted from the naive per-pixel encryption, we propose a house-efficient tiling theme that allows tile-level image scaling and cropping operations. Basically, rather than encrypting every pixel individually, we have a tendency to can encrypt a tile of pixels. 2DCrypt is such that multiple users can read or process the photographs without sharing any encryption keys-a demand desirable for sensible deployments in real organizations. Our analysis and results show that 2DCrypt is INDistinguishable under Chosen Plaintext Attack secure and incurs a suitable overhead. When scaling a 512×512 image by a factor of two, 2DCrypt needs an image user to download approximately five.3 times a lot of data than the un-encrypted scaling and would like to work approximately two.3 s additional for getting the scaled image during a plaintext. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Mining the Most Influential k-Location Set From Massive Trajectories - 2017 A Crowdsourcing Worker Quality Evaluation Algorithm on Mapreduce for Big Data Applications - 2016