research-article

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Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings

Authors:

Vinith M. Suriyakumar,

Nicolas Papernot,

Anna Goldenberg,

Marzyeh GhassemiAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Pages 723 - 734

https://doi.org/10.1145/3442188.3445934

Published: 01 March 2021 Publication History

Abstract

Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for differentially private (DP) learning provide a general-purpose approach to learn models with privacy guarantees. Modern methods for DP learning ensure privacy through the addition of calibrated noise. The resulting privacy-preserving models are unable to learn too much information about the tails of a data distribution, resulting in a loss of accuracy that can disproportionately affect small groups. In this paper, we study the effects of DP learning in health care. We use state-of-the-art methods for DP learning to train privacy-preserving models in clinical prediction tasks, including x-ray classification of images and mortality prediction in time series data. We use these models to perform a comprehensive empirical investigation of the tradeoffs between privacy, utility, robustness to dataset shift and fairness. Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data. We discuss the costs and benefits of differentially private learning in health care with open directions for differential privacy, machine learning and health care.

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Khattak FSubasri VKrishnan APou-Prom CAkinli-Kocak SDolatabadi EPandya DSeyyed-Kalantari LRudzicz F(2025)MLHOps: Machine Learning Health OperationsIEEE Access10.1109/ACCESS.2024.352127913(20374-20412)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3521279
Cho HFroelicher DDokmai NNandi ASadhuka SHong MBerger B(2024)Privacy-Enhancing Technologies in Biomedical Data ScienceAnnual Review of Biomedical Data Science10.1146/annurev-biodatasci-120423-1201077:1(317-343)Online publication date: 23-Aug-2024
https://doi.org/10.1146/annurev-biodatasci-120423-120107
Calvi AMalgieri GKotzinos D(2024)The unfair side of Privacy Enhancing Technologies: addressing the trade-offs between PETs and fairnessProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659024(2047-2059)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659024
Duddu VSzyller SAsokan N(2024)SoK: Unintended Interactions among Machine Learning Defenses and Risks2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00243(2996-3014)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00243
Saranya KValarmathi A(2024)Securing Medical Data in Cloud-Based IoT Networks Using Deep Learning Techniques2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)10.1109/ICUIS64676.2024.10866760(1208-1217)Online publication date: 12-Dec-2024
https://doi.org/10.1109/ICUIS64676.2024.10866760
Baek IChung Y(2024)Differentially private and explainable boosting machine with enhanced utilityNeurocomputing10.1016/j.neucom.2024.128424(128424)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.128424
Tran ALuong THuynh V(2024)A comprehensive survey and taxonomy on privacy-preserving deep learningNeurocomputing10.1016/j.neucom.2024.127345576(127345)Online publication date: Apr-2024
https://doi.org/10.1016/j.neucom.2024.127345
Bennett RHemmati MRamesh RRazzaghi T(2024)Artificial Intelligence and Machine Learning in Precision Health: An Overview of Methods, Challenges, and Future DirectionsDynamics of Disasters10.1007/978-3-031-74006-0_2(15-53)Online publication date: 24-Dec-2024
https://doi.org/10.1007/978-3-031-74006-0_2
Bondi-Kelly EHartvigsen TSanneman LSankaranarayanan SHarned ZWickerson GGichoya JOakden-Rayner LCeli LLungren MShah JGhassemi M(2023)Taking Off with AI: Lessons from Aviation for HealthcareProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623224(1-14)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3617694.3623224
Li YPurcell MRakotoarivelo TSmith DRanbaduge TNg K(2023)Private Graph Data Release: A SurveyACM Computing Surveys10.1145/356908555:11(1-39)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3569085
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