A Survey on Searchable Encryption Techniques with Enhanced Attribute-Based Models

DOI

10.2991/978-94-6239-616-6_100How to use a DOI?

Keywords

Searchable Encryption; Attribute-Based Encryption; Cloud Security; Access Control; Data Privacy

Abstract

Searchable Encryption (SE) has become an important tool for secure data outsourcing. It allows keyword search operations on encrypted cloud data without exposing sensitive information. As Cloud Computing, Big Data, and the Internet of Things (IoT) grow, traditional encryption methods are less effective. They keep data private but can limit how it is used. Modern contexts require both privacy and easy access. Organizations and individuals must protect large amounts of sensitive data while ensuring quick retrieval. SE meets this need by balancing security and usability. However, significant challenges still exist. These include limited query expressiveness, high computational and communication costs, weak access control enforcement, ineffective revocation methods, and vulnerability to leakage attacks. Many foundational and advanced SE schemes have been examined, such as SSE, PEKS, ABE, ABSE, HE, blockchain-based SE, TEE assisted SE, forward and backward privacy preserving SE, multi-authority ABSE, and verifiable SE schemes. Each of these schemes has its design philosophy, security concepts, efficiency trade-offs, and relevance to large real-world applications. Additionally, this study provides a comparative assessment across various aspects like computation overhead, scalability, revocation efficiency, detailed access control, and the verifiability of search results. This research highlights the ongoing need for strong, scalable, and flexible SE methods. These methods should effectively protect against new threats while allowing secure and efficient data use in cloud, IoT, and healthcare areas.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Vijayakumar
AU  - M. Ashigha
AU  - G. Harshavardhini
AU  - G. Shobika
PY  - 2026
DA  - 2026/03/31
TI  - A Survey on Searchable Encryption Techniques with Enhanced Attribute-Based Models
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1360
EP  - 1385
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_100
DO  - 10.2991/978-94-6239-616-6_100
ID  - Vijayakumar2026
ER  -