Dynamic Encryption-Based Cloud Security Model using Facial Image and Password-based Key Generation for Multimedia Data

TL;DR

This paper introduces a dynamic, type-specific encryption model for multimedia data in cloud environments, utilizing facial image and password-based key generation to enhance security and adaptability.

Contribution

It proposes a novel encryption architecture that adapts to different multimedia file types using generated keys from facial images and passwords.

Findings

AES provides strong security for images.

AES-CTR enables real-time streaming of audio/video.

Performance varies across datasets, with DATASET-1 performing best.

Abstract

In this cloud-dependent era, various security techniques, such as encryption, steganography, and hybrid approaches, have been utilized in cloud computing to enhance security, maintain enormous storage capacity, and provide ease of access. However, the absence of data type-specific encryption and decryption procedures renders multimedia data vulnerable. To address this issue, this study presents a dynamic encryption-based security architecture that adapts encryption methods to any file type, using keys generated from facial images and passwords. Four diverse datasets are created, each with a consistent size of 2GB, containing varying combinations of image, audio (MP3 and MPEG), video, text, CSV, PPT, and PDF files, to implement the proposed methodology. AES is used to encrypt image data, AES-CTR is employed for audio or video data to meet real-time streaming needs, and Blowfish is used for other types of data. Performance analysis on all four datasets is conducted using AWS servers, where DATASET-1 demonstrates the best performance compared to the others.