Multi-level encryption algorithm for user-related information across social networks

Lijie Yin, Nasruddin Hassan

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)989-999
Number of pages11
JournalOpen Physics
Volume16
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

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pseudorandom sequences
requirements
sensitivity

Keywords

  • Across social network
  • Chaotic mapping model
  • Inverse propagation
  • Multi-level encryption
  • Neural network
  • User-related information

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Multi-level encryption algorithm for user-related information across social networks. / Yin, Lijie; Hassan, Nasruddin.

In: Open Physics, Vol. 16, No. 1, 01.01.2018, p. 989-999.

Research output: Contribution to journalArticle

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