Shifted Adaption Homomorphism Encryption for Mobile and Cloud Learning. (January 2018)

• Record Type: Journal Article
• Authors: Kalpana, G.; Kumar, P.V.; Aljawarneh, Shadi; Krishnaiah, R.V.
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 178
• Page End: 195
• Publication Date: 2018-01
• Subjects: Mobile and Cloud Learning -- Public key -- Shared key -- Homomorphic Encryption -- Inverted or reversed; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.022
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Mobile learning when stored in a cloud server allows contents to be gathered and accessed using mobile devices connected with the cloud. The present problems of limited computing capacity and small space for storage in mobile phones has inspired the blend of mobile learning and cloud computing. This paper primarily focuses on Homomorphic Encryption to achieve privacy over encoded data or search the encrypted information, which is the current research area of majority of the knowledge experts. In this paper, we suggest Shifted Adaption Homomorphism Encryption (SAHE), which is regarded as the better option for all the current research going on. SAHE implements the smallest public key of 32 bit and is able to encrypt integer and real numbers. A major issue in this field of research is difficulty in protecting user's questions, which is addressed by conceiving a public key encryption technique which is based on the reversed index. Our schema preserves search efficiency using inverted index, by solving one time only search drawback encountered in earlier research works. This method is appropriate for mobile learning since the suggested algorithm will not use the mobile memory or power.