Difference between revisions of "Privacy Preserving Morphological Operations for Digital Images"
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Image processing operations 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 raises privacy concerns. To address these concerns, the data (i.e. the images) owned by the user 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. | Image processing operations 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 raises privacy concerns. To address these concerns, the data (i.e. the images) owned by the user 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. | ||
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| + | In the paper <ref> C. Lupascu, C. Plesca, M. Togan, Privacy Preserving Morphological Operations for Digital Images, 2020 13th International Conference on Communications (COMM), Bucharest, Romania, 2020, pp. 183-188, doi: 10.1109/COMM48946.2020.9141997. </ref> the authors study the possibility of performing some morphological operations on encrypted digital images. | ||
| + | The chosen operations, when represented as plaintext operations in the field <math>\mathbb F_2</math> are modelled by very sparse polynomials of large degree. This makes them very suitable for performing them homomorphically using the [[CSGN]] scheme. | ||
Revision as of 09:10, 22 February 2021
Image processing operations 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 raises privacy concerns. To address these concerns, the data (i.e. the images) owned by the user 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.
In the paper [1] the authors study the possibility of performing some morphological operations on encrypted digital images.
The chosen operations, when represented as plaintext operations in the field are modelled by very sparse polynomials of large degree. This makes them very suitable for performing them homomorphically using the CSGN scheme.
- ↑ C. Lupascu, C. Plesca, M. Togan, Privacy Preserving Morphological Operations for Digital Images, 2020 13th International Conference on Communications (COMM), Bucharest, Romania, 2020, pp. 183-188, doi: 10.1109/COMM48946.2020.9141997.
