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With the development of cloud computing, the storage and processing ofmassive visual media data has gradually transferred to the cloud server. Forexample, if the intelligent video monitoring system cannot process a largeamount of data locally, the data will be uploaded to the cloud. Therefore, howto process data in the cloud without exposing the original data has become animportant research topic. We propose a single-server version of somewhathomomorphic encryption cryptosystem based on confused modulo projection theoremnamed CMP-SWHE, which allows the server to complete blind data processingwithout \emph{seeing} the effective information of user data. On the clientside, the original data is encrypted by amplification, randomization, andsetting confusing redundancy. Operating on the encrypted data on the serverside is equivalent to operating on the original data. As an extension, wedesigned and implemented a blind computing scheme of accelerated version basedon batch processing technology to improve efficiency. To make this algorithmeasy to use, we also designed and implemented an efficient general blindcomputing library based on CMP-SWHE. We have applied this library to foregroundextraction, optical flow tracking and object detection with satisfactoryresults, which are helpful for building smart cities. We also discuss how toextend the algorithm to deep learning applications. Compared with otherhomomorphic encryption cryptosystems and libraries, the results show that ourmethod has obvious advantages in computing efficiency. Although our algorithmhas some tiny errors ($10^{-6}$) when the data is too large, it is veryefficient and practical, especially suitable for blind image and videoprocessing.

Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities