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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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BloomSec: Scalable and privacy-preserving searchable encryption for cloud environments.

Abdul Nasir Khan¹, Ayesha Naveed¹, Abid Mehmood²

¹COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.

|December 9, 2025

Summary

This summary is machine-generated.

This study introduces BloomSec, a novel approach to secure searchable encryption (SSE) for large datasets. BloomSec significantly enhances efficiency and scalability compared to Labeled Searchable Encryption (LSE), offering practical usability without compromising security.

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Area of Science:

Cloud Computing Security
Cryptography
Data Management

Background:

Cloud services offer flexibility but introduce security risks like data breaches.
Existing searchable encryption methods struggle with data leakage or user overhead.
Labeled Searchable Encryption (LSE) is secure but inefficient for large datasets.

Purpose of the Study:

To enhance the efficiency and security of Secure Searchable Encryption (SSE) for large datasets.
To address the limitations of LSE in managing large-scale data.
To develop a practical SSE solution for modern data-driven applications.

Main Methods:

Investigated and developed novel approaches for SSE.
Proposed and evaluated a new scheme called BloomSec.
Compared BloomSec's performance against Labeled Searchable Encryption (LSE).

Main Results:

BloomSec demonstrated significantly higher efficiency and scalability than LSE.
The proposed BloomSec scheme consumes less user-side overhead.
Experimental findings confirm BloomSec's practical utility for large datasets.

Conclusions:

BloomSec offers a secure and efficient solution for searchable encryption on large datasets.
The developed approach overcomes the limitations of existing methods like LSE.
BloomSec provides a practical and scalable method for secure cloud data management.