Multi-level encryption algorithm for user-related information across social networks
The traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related inform...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2018-12-01
|
| Series: | Open Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/phys-2018-0120 |
| _version_ | 1818449957121687552 |
|---|---|
| author | Yin Lijie Hassan Nasruddin |
| author_facet | Yin Lijie Hassan Nasruddin |
| author_sort | Yin Lijie |
| collection | DOAJ |
| description | The traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps. |
| first_indexed | 2024-12-14T20:43:39Z |
| format | Article |
| id | doaj.art-919fc8c09ea94aafa40c45a823d8f737 |
| institution | Directory Open Access Journal |
| issn | 2391-5471 |
| language | English |
| last_indexed | 2024-12-14T20:43:39Z |
| publishDate | 2018-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj.art-919fc8c09ea94aafa40c45a823d8f7372022-12-21T22:48:10ZengDe GruyterOpen Physics2391-54712018-12-0116198999910.1515/phys-2018-0120phys-2018-0120Multi-level encryption algorithm for user-related information across social networksYin Lijie0Hassan Nasruddin1School of Information Engineering, Hebei Geo University, Shijiazhuang, 050031, ChinaSchool of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia UKM Bangi, Selangor, MalaysiaThe traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps.https://doi.org/10.1515/phys-2018-0120across social networkuser-related informationmulti-level encryptionchaotic mapping modelneural networkinverse propagation07.05.mh89.20.ff05.45.pq |
| spellingShingle | Yin Lijie Hassan Nasruddin Multi-level encryption algorithm for user-related information across social networks Open Physics across social network user-related information multi-level encryption chaotic mapping model neural network inverse propagation 07.05.mh 89.20.ff 05.45.pq |
| title | Multi-level encryption algorithm for user-related information across social networks |
| title_full | Multi-level encryption algorithm for user-related information across social networks |
| title_fullStr | Multi-level encryption algorithm for user-related information across social networks |
| title_full_unstemmed | Multi-level encryption algorithm for user-related information across social networks |
| title_short | Multi-level encryption algorithm for user-related information across social networks |
| title_sort | multi level encryption algorithm for user related information across social networks |
| topic | across social network user-related information multi-level encryption chaotic mapping model neural network inverse propagation 07.05.mh 89.20.ff 05.45.pq |
| url | https://doi.org/10.1515/phys-2018-0120 |
| work_keys_str_mv | AT yinlijie multilevelencryptionalgorithmforuserrelatedinformationacrosssocialnetworks AT hassannasruddin multilevelencryptionalgorithmforuserrelatedinformationacrosssocialnetworks |