A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain
Reversible data hiding in the encrypted domain (RDH-ED) is a technique that protects the privacy of multimedia in the cloud service. In order to manage three-dimensional (3D) models, a novel RDH-ED based on prediction error expansion (PEE) is proposed. First, the homomorphic Paillier cryptosystem is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/6/1090 |
| _version_ | 1797529537922203648 |
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| author | Ting Luo Li Li Shanqin Zhang Shenxian Wang Wei Gu |
| author_facet | Ting Luo Li Li Shanqin Zhang Shenxian Wang Wei Gu |
| author_sort | Ting Luo |
| collection | DOAJ |
| description | Reversible data hiding in the encrypted domain (RDH-ED) is a technique that protects the privacy of multimedia in the cloud service. In order to manage three-dimensional (3D) models, a novel RDH-ED based on prediction error expansion (PEE) is proposed. First, the homomorphic Paillier cryptosystem is utilized to encrypt the 3D model for transmission to the cloud. In the data hiding, a greedy algorithm is employed to classify vertices of 3D models into reference and embedded sets in order to increase the embedding capacity. The prediction value of the embedded vertex is computed by using the reference vertex, and then the module length of the prediction error is expanded to embed data. In the receiving side, the data extraction is symmetric to the data embedding, and the range of the module length is compared to extract the secret data. Meanwhile, the original 3D model can be recovered with the help of the reference vertex. The experimental results show that the proposed method can achieve greater embedding capacity compared with the existing RDH-ED methods. |
| first_indexed | 2024-03-10T10:15:15Z |
| format | Article |
| id | doaj.art-12de7251db0943e9ace90c8b030d10d4 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T10:15:15Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-12de7251db0943e9ace90c8b030d10d42023-11-22T00:50:29ZengMDPI AGSymmetry2073-89942021-06-01136109010.3390/sym13061090A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption DomainTing Luo0Li Li1Shanqin Zhang2Shenxian Wang3Wei Gu4Collage of Science and Technology, Ningbo University, Ningbo 315000, ChinaDepartment of Computer Science, Hangzhou Dianzi University, Hangzhou 330018, ChinaDepartment of Computer Science, Hangzhou Dianzi University, Hangzhou 330018, ChinaDepartment of Computer Science, Hangzhou Dianzi University, Hangzhou 330018, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230039, ChinaReversible data hiding in the encrypted domain (RDH-ED) is a technique that protects the privacy of multimedia in the cloud service. In order to manage three-dimensional (3D) models, a novel RDH-ED based on prediction error expansion (PEE) is proposed. First, the homomorphic Paillier cryptosystem is utilized to encrypt the 3D model for transmission to the cloud. In the data hiding, a greedy algorithm is employed to classify vertices of 3D models into reference and embedded sets in order to increase the embedding capacity. The prediction value of the embedded vertex is computed by using the reference vertex, and then the module length of the prediction error is expanded to embed data. In the receiving side, the data extraction is symmetric to the data embedding, and the range of the module length is compared to extract the secret data. Meanwhile, the original 3D model can be recovered with the help of the reference vertex. The experimental results show that the proposed method can achieve greater embedding capacity compared with the existing RDH-ED methods.https://www.mdpi.com/2073-8994/13/6/10903D modelRDH-EDhomomorphic encryptiongreedy algorithmprediction error expansion |
| spellingShingle | Ting Luo Li Li Shanqin Zhang Shenxian Wang Wei Gu A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain Symmetry 3D model RDH-ED homomorphic encryption greedy algorithm prediction error expansion |
| title | A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain |
| title_full | A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain |
| title_fullStr | A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain |
| title_full_unstemmed | A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain |
| title_short | A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain |
| title_sort | novel reversible data hiding method for 3d model in homomorphic encryption domain |
| topic | 3D model RDH-ED homomorphic encryption greedy algorithm prediction error expansion |
| url | https://www.mdpi.com/2073-8994/13/6/1090 |
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