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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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“More generally, several other applications in data analysis may be enhanced, in terms of privacy, through homomorphic encryption, such as predictive analysis on encrypted medical data [46].”
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“While several works [12, 2, 9] discuss the theoretical opportunities of applying fully homomorphic encryption (FHE) in the e-health cloud, this applied research is one of the few attempts discussing a concrete and practical implementation of a mobile and wearable proof-of-concept involving 2…”
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Du, W., Li, A., & Li, Q. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-01701-9_30 · Cited by 21
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A Secure Encryption-Based Malware Detection System
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“There are also many use of Fully Homomorphic Encryption (FHE) or SomeWhat Homomorphic Encryption (SWHE) in genomic data [5].”
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Recognition Over Encrypted Faces
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“Therefore, we can assume the application of our proposed method in real-world applications, such as searching for encrypted genomic data [34], analyzing medical data [35], or outsourcing the database of a company to a public cloud [36].”
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Khazaei, H., McGregor, C., Eklund, J., & El‐Khatib, K. (2015) · JMIR Medical Informatics · doi:10.2196/medinform.4640 · Cited by 15
“A more robust approach may apply encryption and perform analytics on encrypted data [53].”
Ring-LWE Cryptography for the Number Theorist
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Homomorphic SIM$$^2$$D Operations: Single Instruction Much More Data
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Zhu, D., Zhu, H., Liu, X., Li, H., Wang, F., & Li, H. (2018) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2018.8487422 · Cited by 13
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Privacy-Preserving Blockchain-Based Nonlinear SVM Classifier Training for Social Networks
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“[20] proposed a scheme to privately conduct medical predictive analysis tasks on encrypted data.”
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ARITHMETIC USING WORD-WISE HOMOMORPHIC ENCRYPTIONPreprint
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Non-Malleable Functions and Their Applications
Chen, Y., Qin, B., Zhang, J., Deng, Y., & Chow, S. S. M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-49387-8_15 · Cited by 12
Preserving Patient Privacy During Computation over Shared Electronic Health Record Data
d’Aliberti, O. G., & Clark, M. A. (2022) · Journal of Medical Systems · doi:10.1007/s10916-022-01865-5 · Cited by 12
“Similarly, an LHE scheme is used in a near real-time ECG-data monitoring system to securely compute the aver - age heart rate of a patient in a cloud computing environment in [ 24 ] and to carry out linear regression model predictions in [ 25 ].”
MethodInformatics methods in medical privacy
Gkoulalas-Divanis, A., Loukides, G., Xiong, L., & Sun, J. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.07.010 · Cited by 12
Andrew, J., Mathew, S. S., & Mohit, B. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1362/1/012070 · Cited by 12
“[13] explores the conceivable chain of events for homomorphic encryptions as a result to ensure the privacy of sensitive medical data.”
BackgroundPrivacy preservation of genome data analysis using homomorphic encryption
Kachouh, B., Hariss, K., Sliman, L., Samhat, A. E., & Alsuliman, T. (2021) · Service Oriented Computing and Applications · doi:10.1007/s11761-021-00326-0 · Cited by 11
“HE is useful in many modern cloud-based applications, not restricted to genomic data, such as predictive analysis [6], financial applications [7], e-voting [8], and medical healthcare monitoring [9].”
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A distributed approach to emergency demand response in geo-distributed mixed-use buildings
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Low Depth Circuits for Efficient Homomorphic Sorting.Preprint
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“Their system uses a previously built classification model to process new encrypted observations sent to the cloud server 10 .”
MethodPrivacy-Preserving Multi-Party Access Control
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“Secure computation has been applied in a large range of domains, spanning from medical data analysis [6] and face recognition [16], to constructing classifiers [7] and clustering algorithms [37].”
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Kumar, A., & Kumar, R. (2019) · Springer eBooks · doi:10.1007/978-3-030-22277-2_28 · Cited by 10
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MethodEnhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning
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Homomorphic proximity computation in geosocial networks
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“…performing arbitrary computation with FHE schemes is still far from practical [10], [24], many applications based on SWHE schemes have demonstrated their practicality in the field of biometric authentication [25], medical/genomic data analysis [15], [26], and private information retrieval [27], [4].”
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DP-Poison: Poisoning Federated Learning under the Cover of Differential Privacy
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“This type of attack allows the attacker to determine whether a certain type of dataset was used for model training [31–34].”
Practical personalized genomics in the encrypted domain
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“[3] proposed a working implementation of cloud service for private computation of encrypted health data using FHE.”
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Shao, Y., Tian, C., Han, L., Xian, H., & Yu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3181734 · Cited by 8
“Therefore, a plenty of subsequent techniques [2], [3], [30] have been developed to”
MethodApplication of Homomorphic Encryption in Machine Learning
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Secure numerical simulations using fully homomorphic encryption
Kholod, A., Polyakov, Y., & Schlottke-Lakemper, M. A. (2025) · Computer Physics Communications · doi:10.1016/j.cpc.2025.109868 · Cited by 8
Deng, W., Zhang, L., Yue, J.-R., & Huang, X.-L. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1691682 · Cited by 10
“End-to-end encryption (E2EE) is widely used to secure data during transmission, and homomorphic encryption is increasingly applied in healthcare data processing, allowing computations on encrypted data without decryption (78, 79), thereby enabling analysis without compromising patient privacy (78).”
Secure Testing for Genetic Diseases on Encrypted Genomes with Homomorphic Encryption Scheme
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“In particular in the health sector, sensitive personal patient records need to be kept confidential [3].”
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“Additionally, homomorphic encryption can be used to ensure privacyfor computational tasks involving highly sensitive data, such as sensitive medical and genomic data 75 .”
A review of recent advances in Privacy preservation in health care data publishing
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Privacy Set: Privacy-Authority-Aware Compiler for Homomorphic Encryption on Edge-Cloud System
Kim, D., Lee, Y., Cheon, S., Choi, H., Lee, J., Youm, H., Lee, D., & Kim, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3437356 · Cited by 6
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BackgroundRetractedPrivacy-Preserving Feature Selection with Fully Homomorphic Encryption
Ono, S., Takata, J., Kataoka, M., Tomohiro, I., Shin, K., & Sakamoto, H. (2022) · Algorithms · doi:10.3390/a15070229 · Cited by 6
Protecting Attacks on Unmanned Aerial Vehicles using Homomorphic Encryption
Alzahrani, M. Y., Khan, N. A., Georgieva, L., Bamahdi, A. M., Abdulkader, O. A., & Alahmadi, A. H. (2023) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v11i1.3932 · Cited by 6
Preserving Genome Privacy in Research Studies
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On the Use of XML in Medical Imaging Web-Based Applications
Mata, C., Oliver, A., Lalande, A., Walker, P. M., & Martı́, J. (2016) · IRBM · doi:10.1016/j.irbm.2016.10.001 · Cited by 6
Privacy‐ensuring electronic health records in the cloud
Souza, S. M. P. C., Gonçalves, R. F., Leonova, E., Puttini, R., & Nascimento, A. C. A. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4045 · Cited by 6
Medical Diagnostics Based on Encrypted Medical Data
Gribov, A., Horan, K., Gryak, J., Najarian, K., Shpilrain, V., Soroushmehr, S. M. R., & Kahrobaei, D. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-24202-2_8 · Cited by 6
“Bos et al. [26] also utilize a leveled homomorphic encryption scheme.”
BackgroundFully Homomorphic Encryption: An Antidote to Cloud Data Security and Privacy Concerns
Jubrin, A. M., Izegbu, I., & Adebayo, O. S. (2019) · International Conference on Electronics, Computer and Computation · doi:10.1109/icecco48375.2019.9043280 · Cited by 6
“However, traditional encryption methods which requires the need to decrypt the ciphertext prior to carrying out computations on it limits the intents to outsource computations on the externally stored information because this private decryption key does not reside with the data centres [4].”
MethodPrivacy Preserving Predictive Analytics with Smart Meters
Habtemariam, B., Miranskyy, A. V., Miri, A., Samet, S., & Davison, M. (2016) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2016.31 · Cited by 6
“Similar privacy issues in other domains—such as, health care with biomedical informatics [3], retail customer data [7], and general privacy-sensitive data set [2], [4], [8], [21]—have also been investigated.”
BackgroundZKSENSE: a Privacy-Preserving Mechanism for Bot Detection in Mobile DevicesPreprint
Papadopoulos, P., Querejeta-Azurmendi, I., Zhang, J., Varvello, M., Nappa, A., & Livshits, B. (2019) · arXiv (Cornell University) · Cited by 6
Sun, P., Cai, C., Zhang, Y., Yip, D. W., & Esmradi, A. (2024) · Marine Policy · doi:10.1016/j.marpol.2024.106244 · Cited by 5
Confidential and Protected Disease Classifier using Fully Homomorphic Encryption
Malik, A., Ratha, N., Yalavarthi, B., Sharma, T., Kaushik, A., & Jutla, C. S. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00074 · Cited by 5
“An intriguing illustration involves encrypting EHRs and uploading them to the cloud, where encrypted records are employed to inform users of their likelihood of experiencing cardiovascular disease [21] [22].”
Enabling the Informed Patient Paradigm with Secure and Personalized Medical Question Answering
Oduro-Afriyie, J., & Jamil, H. M. (2023) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3584371.3613016 · Cited by 5
“Such techniques have been applied to various healthcare scenarios, such as genomic data analysis [17], medical image processing [12], and electronic health record analysis [4].”
MethodExperimental Evaluation of Homomorphic Encryption in Cloud and Edge Machine Learning
Hrzich, J., Basra, G., & Halabi, T. (2022) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse56063.2022.9953624 · Cited by 5
“Bost et al. [37] and Bos et al. [12] looked at FHE and computed medical prediction functions in regard to medical data.”
MethodPIE: p-adic Encoding for High-Precision Arithmetic in Homomorphic Encryption
Harmon, L., Delavignette, G., Roy, A., & da Silva, D. W. H. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33488-7_16 · Cited by 5
“Previous works [3,4,6,10,11,21,27] have proposed several encoding methods for integers and rationals.”
MethodPrivacy-preserving biomedical data dissemination via a hybrid approach.
Jiang, Y., Wang, C., Wu, Z., Du, X., & Wang, S. (2018) · Europe PMC (PubMed Central) · Cited by 6
A Moment Matching Approach for Generating Synthetic Data
Bogle, B. M., & Mehrotra, S. (2016) · Big Data · doi:10.1089/big.2016.0015 · Cited by 4
“12 A practical implementation of leveled homomorphic encryption of logistic and Cox models on encrypted cardiovascular data is described by Bos et al. 13 Graepel et al. 14 demonstrated that some machine learning algorithms can be trained on encrypted data on small databases.”
Secure Image Inference Using Pairwise Activation Functions
Agyepong, J. T., Soliman, M. I., Wada, Y., Kimura, K. R., & El-Mahdy, A. (2021) · IEEE Access · doi:10.1109/access.2021.3106888 · Cited by 4
“That allowed them to remove the limitation induced by an input range, making their analyses faster and more accurate than [17].”
BackgroundMethodPPNNBP: A Third Party Privacy-Preserving Neural Network With Back-Propagation Learning
Almutairi, N., Coenen, F., & Dures, K. (2023) · IEEE Access · doi:10.1109/access.2023.3263114 · Cited by 4
“The operation of the sigmoid activation function can be approximated, up to a certain accuracy, using a polynomial approximation method that uses: (i) Taylor series expansions [17], [43], (ii) Chebyshev polynomials [18], [41], and (iii) Maclaurin series expansions [44].”
MethodData Analytics over Encrypted Data from Fully Homomorphic Encryption
Mittal, S., & Singh, S. (2023) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon58499.2023.10269997 · Cited by 4
“By fitting data to a logistic curve, the binary logistic regression model evaluates the likelihood of an event occurring [2][4].”
MethodAn Efficient Federated Learning Framework for Privacy-Preserving Data Aggregation in IoT
Shi, R., Wei, L., & Zhang, L. (2023) · Conference on Privacy, Security and Trust · doi:10.1109/pst58708.2023.10320198 · Cited by 4
Application of Homomorphic Encryption in Machine Learning Based Chronic Kidney Disease Prediction
Gogoi, P., & Valan, J. A. (2024) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf61366.2024.10692276 · Cited by 4
“By training the model on normal data and then applying it to encrypted data for prediction, we can leverage the benefits of faster training times while still preserving privacy during inference [23].”
MethodKumari, K. A., Indusha, M., & Dharani, D. (2021) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet51464.2021.9456309 · Cited by 4
“The main drawback is that it restricts the operations over arithmetic circuits within these polynomials [8].”
BackgroundSecure Predictive Analysis on Heart Diseases Using Partially Homomorphic Machine Learning Model
Boomija, M. D., & Raja, S. V. K. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-19-0332-8_42 · Cited by 4
Quantum Cryptography for Securing Personal Health Information in Hospitals
Maheshwari, A., & Mantry, H. (2022) · Newborn · doi:10.5005/jp-journals-11002-0043 · Cited by 4
Cloud-Assisted Privacy-Preserving Classification for IoT Applications
Yang, L., & Li, F. (2018) · IEEE Conference on Communications and Network Security · doi:10.1109/cns.2018.8433157 · Cited by 4
“classification problem [8], [9], [10].”
BackgroundMethodResultInfluentialData confidentiality in cloud-based pervasive system
Khan, K. M., Shaheen, M., & Wang, Y. (2017) · International Conference on Internet of Things, Data and Cloud Computing (ICC) · doi:10.1145/3018896.3056802 · Cited by 4
“758 s 240 s (number of share = 2) (4 min ) Fully Homomorphic encryption[4] 51 minutes 23 days 161 days Several years (Used 80-bit security) It also does not know which of the matrices contain the real values.”
BackgroundMethodDelegating Elliptic-Curve Operations with Homomorphic Encryption
Melchor, C. A., Deneuville, J.-C., Gaborit, P., Lepoint, T., & Ricosset, T. (2018) · IEEE Conference on Communications and Network Security · doi:10.1109/cns.2018.8433140 · Cited by 4
“Open-source libraries for homomorphic encryption are available online [25, 19, 20, 29], and were used as building blocks in several prototypes in the recent years, such as statistics computations [30], machine learning [18], signal processing [1], database queries [5, 11], private health diagnosis [6], genome statisti…”
MethodA Fully Private Pipeline for Deep Learning on Electronic Health RecordsPreprint
Chou, E., Nguyen, T. N. N., Beal, J., Haque, A., & Fei-Fei, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.09951 · Cited by 4
“[7] uses homomorphic encryption to encrypt a linear regression model trained on medical databases.”
Secure Neural Network Prediction in the Cloud-Based Open Neural Network Service
Huang, W., Zhang, G., Liao, Y., Peng, J., Huang, F., & Yang, J. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3332744 · Cited by 3
“Patients’medicaldatashouldbekeptsecret,especially sensitivepersonaldatalikeDNAsequence[8].”
Private Genome Analysis through Homomorphic Encryption.
Kim, M., & Lauter, K. (2015) · Cited by 3
Semantic wikis as flexible database interfaces for biomedical applications
Falda, M., Atzori, M., & Corbetta, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-27743-9 · Cited by 3
“More pervasive data alterations should be limited since analyses on encrypted data would be more complex to manage 50 .”
BackgroundZhang, R., Chow, C., & Hui, L. C. K. (2017) · International Symposium on Pervasive Systems, Algorithms and Networks (ISPAN) · doi:10.1109/ispan-fcst-iscc.2017.56 · Cited by 3
“[11] proposed using homomorphic encryption schemes to encrypt the EHRs.”
SecureBP from Homomorphic Encryption
Liu, Q., Lu, X., Luo, F., Zhou, S., He, J., & Wang, K. (2020) · Security and Communication Networks · doi:10.1155/2020/5328059 · Cited by 3
Outsourcing Medical Dataset Analysis: A Possible Solution
Kaptchuk, G., Green, M., & Rubin, A. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70972-7_6 · Cited by 3
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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