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Hybrid short comparable encryption with sliding window techniques for enhanced efficiency and security
Department of Information Technology, University of the Cumberlands, Williamsburg, KY, USA.
Research Article
International Journal of Science and Research Archive, 2022, 05(01), 151-161.
Article DOI: 10.30574/ijsra.2022.5.1.0024
Publication history:
Received on 15 December 2021; revised on 26 January 2022; accepted on 28 January 2022
Abstract:
The existing Short Comparable Encryption (SCE) schemes ensure the security of IoT data while allowing ciphertext comparison to infer plaintext relationships. However, these schemes incur significant computational and storage overhead during ciphertext comparison and label generation. To address these challenges, this paper introduces a hybrid approach that combines Short Comparable Encryption with sliding window techniques, resulting in Short Comparable Encryption based on the Sliding Window (SCESW) scheme. The proposed scheme leverages the efficiency of sliding window methods to optimize computational and storage requirements while maintaining robust security guarantees. Through rigorous security analysis, the SCESW scheme is shown to achieve weak indistinguishability under the standard model, ensuring data integrity and confidentiality. Additionally, experimental performance evaluations demonstrate that the storage overhead of the SCESW scheme is reduced to 1/t+1 (Where t > 1) compared to conventional SCE schemes, with a significant improvement in computational efficiency. The integration of sliding window techniques enables the uniform grouping of binary data, further enhancing the scheme's practicality and scalability.
This hybrid approach represents a step forward in secure and efficient data encryption, offering a promising solution for real-world applications such as encrypted database queries, secure auctions, and image retrieval systems.
Keywords:
Hybrid encryption; Short Comparable Encryption; Sliding Window Techniques; Weak Indistinguishability; Data Integrity; Computational Efficiency
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
