Secure and Efficient Keyword-Based Threat Detection

Abstract

Public Key Encryption with Keyword Search (PEKS) is a common cryptographic primitive that facilitates secure keyword search over encrypted data so that the keywords hidden inside are concealed from unauthorized parties. For facilitating more complex search operations, Expressive PEKS (EPEKS) generalizes the fundamental PEKS to accommodate expressive queries like conjunctive and disjunctive keyword search. Provision of these expressive features is usually facilitated by Attribute-Based Encryption (ABE). With additional features, though, current EPEKS systems are susceptible to keyword guessing attacks that compromise the confidentiality of the search queries. This work examines Shen et al.\'s (2019) modified expressive PEKS scheme from a keyword guessing attack vulnerability perspective. To mitigate such security attacks, we present a new improved encryption-decryption model with expressive search functionality but improved guessing attack resistance. Our approach employs a secure means of key sharing, e.g., Diffie-Hellman or Elliptic Curve Diffie-Hellman (ECDH), to facilitate secure communication. We deploy the suggested framework and evaluate its performance and security attributes, demonstrating its capacity to provide expressive search ability as well as resist keyword inference attacks successfully.

Introduction

The rapid rise of cloud computing has increased the need for secure and efficient methods of searching encrypted data. Public Key Encryption with Keyword Search (PEKS) allows users to search encrypted data without decrypting it, ensuring data privacy. To support complex search queries (e.g., AND/OR/Boolean), Expressive PEKS (EPEKS) was introduced. However, EPEKS schemes are vulnerable to keyword guessing attacks, which can expose private search terms.

This work identifies security flaws in Shen et al.’s (2019) EPEKS model and proposes a more secure and efficient alternative using Diffie-Hellman (DH) or Elliptic Curve DH (ECDH) key exchange. The new approach improves keyword privacy while maintaining search performance. It uses techniques such as randomized trapdoor generation, non-deterministic encryption, and hash obfuscation to resist adversarial attacks.

Key Contributions:

• A secure EPEKS framework combining Attribute-Based Encryption (ABE) and secure key exchange.

• Real-time, session-based encryption to prevent static trapdoor reuse.

• Defenses against keyword guessing through obfuscation and randomness.

• Scalable and privacy-preserving design suitable for cloud environments.

Literature Insights:

A broad survey of 20 related works highlights:

• Most current PEKS/EPEKS models suffer from scalability, performance, or security limitations.

• Several approaches lack protection against side-channel and keyword-guessing attacks.

• Others are constrained by computational complexity, key management challenges, or lack of real-world implementation.

• Recent advancements introduce lattice-based, blockchain-integrated, and multi-identity encryption schemes, but many remain theoretical or limited in scope.

Proposed Methodology:

1. System Initialization & Key Generation:

   • Generate RSA/ECC key pairs using PyCryptodome.

   • Establish a secure session key via DH or ECDH.

2. Keyword Encryption & Trapdoor Generation:

   • Encrypt keywords using the user’s public key.

   • Generate secure, randomized trapdoors for search queries.

3. Secure Communication:

   • Use session keys for encrypted transmission between users and the server.

   • Protect trapdoors and queries from exposure to prevent inference attacks.

Conclusion

With the age of cloud-based data storage and retrieval, the balance between data privacy and effective search functionality is more important now. While advanced query functionalities such as conjunctive and disjunctive searches have been made possible by Expressive Public Key Encryption with Keyword Search (EPEKS), current models are still susceptible to keyword guessing attacks that violate user privacy. The suggested methodology, with the backing of robust testing and deployment, illustrates that expressive and secure keyword search over encrypted content is feasible. This work is a major improvement towards constructing practical, privacy-enhancing searchable encryption systems applicable to contemporary cloud setting.

References

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Copyright

Copyright © 2025 Nagarathna C, Sakshi Jha, Sharanamma , V Pushpa, Sandhya Urs H. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.