IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Optimal Privacy Preserving Scheme Based on Modified ANN and PSO in Cloud

Optimal Privacy Preserving Scheme Based on Modified ANN and PSO in Cloud
View Sample PDF
Author(s): N.G. Nageswari Amma (Vins Christian College of Engineering, Nagercoil, India)and F. Ramesh Dhanaseelan (Computer Application Department, St Xavier's Catholic, College of Engineering, Nagercoil, India)
Copyright: 2021
Pages: 21
Source title: Research Anthology on Privatizing and Securing Data
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8954-0.ch035

Purchase

View Optimal Privacy Preserving Scheme Based on Modified ANN and PSO in Cloud on the publisher's website for pricing and purchasing information.

Abstract

In the cloud, various privacy-preserving and security threats on data retrieval processes exist. In this article, the authors propose an efficient method for secure privacy preserving in cloud. Initially, the shared file is encrypted using a Vigenere encryption algorithm before uploading. For creating the privacy map, the efficient classification algorithm is recommended. Here, a Modified Artificial Neural Network (MANN) is used to generate the privacy map. The weight value of the neural network is optimized using a Particle Swarm Optimization (PSO) algorithm. While retrieving files initially, the authorization of the person is verified by providing basic information, then the OTP of the respective files is verified. Since the user can retrieve the files only after authorization, verification and decryption of the files is highly secured and privacy is preserved. The performance of the proposed method is evaluated in terms of time and accuracy.

Related Content

Chaymaâ Boutahiri, Ayoub Nouaiti, Aziz Bouazi, Abdallah Marhraoui Hsaini. © 2024. 14 pages.
Imane Cheikh, Khaoula Oulidi Omali, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 30 pages.
Tahiri Omar, Herrou Brahim, Sekkat Souhail, Khadiri Hassan. © 2024. 19 pages.
Sekkat Souhail, Ibtissam El Hassani, Anass Cherrafi. © 2024. 14 pages.
Meryeme Bououchma, Brahim Herrou. © 2024. 14 pages.
Touria Jdid, Idriss Chana, Aziz Bouazi, Mohammed Nabil Kabbaj, Mohammed Benbrahim. © 2024. 16 pages.
Houda Bentarki, Abdelkader Makhoute, Tőkési Karoly. © 2024. 10 pages.
Body Bottom