ArticleScientific reports2022
Differential privacy medical data publishing method based on attribute correlation.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Machine learning-driven data perturbation techniques for privacy-preserving data mining.Scientific reports · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.NPJ digital medicine · 2026Article
- Enhancing student data privacy in virtual learning with blockchain and advanced encryption.PloS one · 2026Article
- Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings.Scientific reports · 2025Article
- Privacy preserving strategies for electronic health records in the era of large language models.NPJ digital medicine · 2025Article
- A Survey on Differential Privacy for Medical Data Analysis.Annals of data science · 2023Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of the era of big data promotes the further development of medicine, and data release is an important step in it. The existing medical data release methods mostly use the k-anonymity model as the basis for data protection. With the advancement of technology, anonymous models are progressively less resistant to consistency attacks and background knowledge attacks. In order to better protect the private information of patients, this paper makes two major contributions: (1) The method of calculating the correlation between attributes is used to ensure the validity of the data after the data is released; (2) On the basis of the previous step, combined with the difference privacy-preserving model and tree model, this paper proposes an attribute association-based differential privacy classification tree data publishing method (ACDP-Tree). In this paper, simulation experiments are carried out on real medical data sets. The experimental results show that the algorithm ensures the validity and availability of the data to a certain extent while ensuring that the patient's privacy is not leaked.
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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.