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

Naima Sultana Ayesha, Mehrin Anannya, Md Biplob Hosen, Rashed Mazumder

TL;DR

This work tackles cloud storage security for multimedia by introducing a dynamic encryption framework that assigns encryption schemes based on data type and uses a key generated from both facial features and a password. The key is formed by hashing facial landmarks and the password, then combining them with XOR, and the same key regulates AES-ECB for images, AES-CTR for audio/video, and Blowfish for other data. Empirical evaluation on four 1.2GB datasets shows preserved data integrity after encryption/decryption, with Dataset-3 delivering the best throughput and Dataset-2 incurring the highest size overhead. The approach demonstrates practical viability for secure multimedia Cloud storage, while highlighting privacy, liveness, and computational overhead considerations for real-world deployment.

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.

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

TL;DR

This work tackles cloud storage security for multimedia by introducing a dynamic encryption framework that assigns encryption schemes based on data type and uses a key generated from both facial features and a password. The key is formed by hashing facial landmarks and the password, then combining them with XOR, and the same key regulates AES-ECB for images, AES-CTR for audio/video, and Blowfish for other data. Empirical evaluation on four 1.2GB datasets shows preserved data integrity after encryption/decryption, with Dataset-3 delivering the best throughput and Dataset-2 incurring the highest size overhead. The approach demonstrates practical viability for secure multimedia Cloud storage, while highlighting privacy, liveness, and computational overhead considerations for real-world deployment.

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.

Paper Structure

This paper contains 12 sections, 7 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. System Methodology
  4. Key Generation
  5. Encryption and Decryption Based on Data Type
  6. Dataset Description
  7. Result and Discussion
  8. Execution Time for Key Generation
  9. Entropy Analysis
  10. Brute Force Attack Analysis
  11. Encryption / Decryption Performance Analysis
  12. Conclusion

Figures (3)

  • Figure 1: Overview of system architecture.
  • Figure 2: Key Generation Process.
  • Figure 3: Size variation among original, encrypted, and decrypted data.