ArticlePloS one2022
Local differential privacy protection for wearable device data.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- IoT framework for sports activity safety monitoring based on wearable sensors and CRNN spatiotemporal analysis.Scientific reports · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification.Scientific reports · 2025Article
- What Clinicians Should Tell Patients About Wearable Devices and Data Privacy: A Narrative Review.Cureus · 2025Review
- Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.Journal of personalized medicine · 2024Review
- SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies.World neurosurgery: X · 2024Review
- A Survey on Differential Privacy for Medical Data Analysis.Annals of data science · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users' personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.