The University of Southampton
University of Southampton Institutional Repository

Efficient privacy-preserving conjunctive searchable encryption for cloud-IoT healthcare system

Efficient privacy-preserving conjunctive searchable encryption for cloud-IoT healthcare system
Efficient privacy-preserving conjunctive searchable encryption for cloud-IoT healthcare system
In cloud-Internet of Things (IoT) healthcare systems, private medical data leakage is a serious concern as the cloud server is not fully trusted. Dynamic searchable symmetric encryption (DSSE), with necessary forward and backward privacy security properties, enables doctors to retrieve ciphertexts while guaranteeing data privacy. However, existing forward and backward private DSSE schemes are not well-suited for cloud-IoT healthcare systems with attribute-value type databases. To this end, we propose an efficient privacy-preserving conjunctive searchable encryption scheme for cloud-IoT healthcare systems, called PC-SE. It is the first conjunctive DSSE scheme designed for attribute-value type databases. Specifically, we design flexible search capabilities for PC-SE to address users’ various search requirements. It can not only achieve precise conjunctive search based on keywords but also realize broad attribute search. Moreover, our scheme achieves fine-grained search for attribute values while maintaining forward and Type-I− backward privacy. This approach reduces the communication burden and minimizes the risk of privacy exposure. To ensure that users with different authorities can only access the corresponding attribute values, we introduce an attribute access control mechanism in PC-SE. Finally, security analysis and experimental results demonstrate that PC-SE is secure and effective.
2471-2566
Ma, Jiadi
da848ed8-cc80-4599-9d70-0667fd6afd9a
Peng, Tianqi
66b5b12e-9ef2-44c4-9f99-763271bfe30b
Gong, Bei
dd699a78-c0f9-498d-87d4-03f66274f316
Waqas, Muhammad
28f978b5-2da0-4060-aa7c-d5cadc1a48e1
Alasmary, Hisham
5f38ead1-f928-4f7d-bc0d-81a3ccb53034
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Ma, Jiadi
da848ed8-cc80-4599-9d70-0667fd6afd9a
Peng, Tianqi
66b5b12e-9ef2-44c4-9f99-763271bfe30b
Gong, Bei
dd699a78-c0f9-498d-87d4-03f66274f316
Waqas, Muhammad
28f978b5-2da0-4060-aa7c-d5cadc1a48e1
Alasmary, Hisham
5f38ead1-f928-4f7d-bc0d-81a3ccb53034
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Ma, Jiadi, Peng, Tianqi, Gong, Bei, Waqas, Muhammad, Alasmary, Hisham and Chen, Sheng (2025) Efficient privacy-preserving conjunctive searchable encryption for cloud-IoT healthcare system. ACM Transactions on Privacy and Security. (doi:10.1145/3769425).

Record type: Article

Abstract

In cloud-Internet of Things (IoT) healthcare systems, private medical data leakage is a serious concern as the cloud server is not fully trusted. Dynamic searchable symmetric encryption (DSSE), with necessary forward and backward privacy security properties, enables doctors to retrieve ciphertexts while guaranteeing data privacy. However, existing forward and backward private DSSE schemes are not well-suited for cloud-IoT healthcare systems with attribute-value type databases. To this end, we propose an efficient privacy-preserving conjunctive searchable encryption scheme for cloud-IoT healthcare systems, called PC-SE. It is the first conjunctive DSSE scheme designed for attribute-value type databases. Specifically, we design flexible search capabilities for PC-SE to address users’ various search requirements. It can not only achieve precise conjunctive search based on keywords but also realize broad attribute search. Moreover, our scheme achieves fine-grained search for attribute values while maintaining forward and Type-I− backward privacy. This approach reduces the communication burden and minimizes the risk of privacy exposure. To ensure that users with different authorities can only access the corresponding attribute values, we introduce an attribute access control mechanism in PC-SE. Finally, security analysis and experimental results demonstrate that PC-SE is secure and effective.

Text
AMC-TPS2025 - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (5MB)

More information

Accepted/In Press date: 4 September 2025
e-pub ahead of print date: 26 September 2025

Identifiers

Local EPrints ID: 505703
URI: http://eprints.soton.ac.uk/id/eprint/505703
ISSN: 2471-2566
PURE UUID: d8d863b2-0921-4483-be1f-ae8b8b83225c

Catalogue record

Date deposited: 16 Oct 2025 16:53
Last modified: 16 Oct 2025 16:53

Export record

Altmetrics

Contributors

Author: Jiadi Ma
Author: Tianqi Peng
Author: Bei Gong
Author: Muhammad Waqas
Author: Hisham Alasmary
Author: Sheng Chen

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×