Achieving Searchable and Privacy-Preserving Data Sharing for Cloud-Assisted E-Healthcare System
Abstract
The integration of wearable wireless devices and cloud computing in e-health systems has significantly improved their effectiveness and availability. Patients can upload their personal health information (PHI) files to the cloud, from where the health service providers (HSPs) can obtain appropriate information to determine the health state. This system not only reduces the costs associated to healthcare but also provides timely diagnosis to save lives. However, a number of privacy concerns arise while sharing sensitive information. In this paper, we propose a novel privacy-preserving patient health information sharing scheme, which allows HSPs to access and search PHI files in a secure yet efficient manner. We make use of the searchable encryption technique with keyword range search and multikeyword search. The proposed privacy-preserving equality test protocol allows different types of numeric comparison searches on encrypted data. We also use a variant of bloom filter and message authentication code to classify PHI files, filter false data, and check integrity of search results. The simulations on real-world and synthetic data show the feasibility and efficiency of the system, and security analysis proves the privacy-preservation properties.
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By using an inference attack, a malicious user can get what kind of information is being shared, including blood samples, type of disease and other relevant data.
“Furthermore, by using an inference attack by the malicious user, the adversary can get what kind of information is being shared.This might include blood samples, type of disease and other relevant data [50].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [50]
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Enhanced Public-Key Searchable Encryption Scheme for Cloud-Based EHR Systems
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- Authors
- Xu, C., Wang, N., Zhu, L., Sharif, K., & Zhang, C.
- Affiliations
- Beijing Institute of Technology (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-10
- Volume
- 6
- Issue
- 5
- Pages
- 8345-8356
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2945262612
- Cited by
- 105
- References
- 39
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 15 · 2023: 31 · 2022: 28 · 2021: 17 · 2020: 6
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (61402037, 61272512)
- Beijing Institute of Technology (2018CX20013)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- privacy-preserving data sharing, searchable encryption, searchable symmetric encryption, wearable devices, personal health data, Bloom filter, message authentication codes, data integrity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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