Open Access

ARTICLE

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

by Chanhyeong Cho¹, Byeori Kim², Haehyun Cho², Taek-Young Youn^1,*

1 Department of Cyber Security, Dankook University, Yongin, 16890, Republic of Korea
2 Department of Software, Soongsil University, Seoul, 07027, Republic of Korea

* Corresponding Author: Taek-Young Youn. Email:

(This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)

Computer Modeling in Engineering & Sciences 2025, 142(1), 813-834. https://doi.org/10.32604/cmes.2024.056952

Received 03 August 2024; Accepted 23 October 2024; Issue published 17 December 2024

Abstract

With the rise of remote collaboration, the demand for advanced storage and collaboration tools has rapidly increased. However, traditional collaboration tools primarily rely on access control, leaving data stored on cloud servers vulnerable due to insufficient encryption. This paper introduces a novel mechanism that encrypts data in ‘bundle’ units, designed to meet the dual requirements of efficiency and security for frequently updated collaborative data. Each bundle includes updated information, allowing only the updated portions to be re-encrypted when changes occur. The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes, such as Cipher Block Chaining (CBC) and Counter (CTR), which require decrypting and re-encrypting the entire dataset whenever updates occur. The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data. By utilizing this information, the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections. This approach significantly enhances the efficiency of data updates while maintaining high performance, particularly in large-scale data environments. To validate this approach, we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied. Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed, with greater performance gains as data size increases. Additionally, our security evaluation confirms that this method provides robust protection against both passive and active attacks.

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Keywords

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