  With the development of cloud computing, the storage and processing of
massive visual media data has gradually transferred to the cloud server. For
example, if the intelligent video monitoring system cannot process a large
amount of data locally, the data will be uploaded to the cloud. Therefore, how
to process data in the cloud without exposing the original data has become an
important research topic. We propose a single-server version of somewhat
homomorphic encryption cryptosystem based on confused modulo projection theorem
named CMP-SWHE, which allows the server to complete blind data processing
without \emph{seeing} the effective information of user data. On the client
side, the original data is encrypted by amplification, randomization, and
setting confusing redundancy. Operating on the encrypted data on the server
side is equivalent to operating on the original data. As an extension, we
designed and implemented a blind computing scheme of accelerated version based
on batch processing technology to improve efficiency. To make this algorithm
easy to use, we also designed and implemented an efficient general blind
computing library based on CMP-SWHE. We have applied this library to foreground
extraction, optical flow tracking and object detection with satisfactory
results, which are helpful for building smart cities. We also discuss how to
extend the algorithm to deep learning applications. Compared with other
homomorphic encryption cryptosystems and libraries, the results show that our
method has obvious advantages in computing efficiency. Although our algorithm
has some tiny errors ($10^{-6}$) when the data is too large, it is very
efficient and practical, especially suitable for blind image and video
processing.
