Privacy Preserving Morphological Operations for Digital Images

Some image processing operations like segmentation or edge detection usually require complex computations. The emerging cloud technologies provide businesses and individuals with the possibility to outsource these computations on a remote platform with large resources and high availability. This approach substantially reduces the costs, but could rise privacy issues. To address these issues, the user data could be encrypted, sent to the cloud and processed there by taking advantage from the properties of a homomorphic encryption scheme. The result of the computation performed on encrypted data is sent back to the user to be decrypted. This paper studies the possibility of conducting some morphological operations on encrypted digital images. First, we model these processing using the two elementary operations in the ring F
₂
. Second, we implement these computations using CSGNHE which is a recently proposed scheme by our team. Finally, we compare our results in terms of execution times and encrypted data size with some well-known schemes from the literature.