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Private predictive analysis on encrypted medical data
Open accessAbstract
Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key. In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft's Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data.
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“Nevertheless, the system is not sufficiently secure because the transmission of gradients and partial parameters may lead to indirect privacy leakage (Bos et al., 2014).”
From A review of applications in federated learning (2020) · cites this paper as Bos et al., 2014
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Membership Inference Attacks Against Machine Learning Models
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BackgroundMethodInfluentialPrivacy-preserving deep learning
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“Nevertheless, the system is not sufficiently secure because the transmission of gradients and partial parameters may lead to indirect privacy leakage (Bos et al., 2014).”
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Background
Metadata
- Authors
- Bos, J. W., Lauter, K., & Naehrig, M.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Lauter, Kristin
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-08
- Volume
- 50
- Pages
- 234-243
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2077905990
- Cited by
- 283
- References
- 44
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 24 · 2023: 21 · 2022: 16 · 2021: 23 · 2020: 21 · 2019: 36 · 2018: 31 · 2017: 27 · 2016: 31 · 2015: 25 · 2014: 4
- FWCI
- 28.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- homomorphic encryption, cardiovascular disease prediction, health data privacy, Microsoft Azure
- MeSH terms
- Proportional Hazards Models, Medical Records Systems, Computerized, Computer Security, Privacy
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