| Autor: |
WANG Mei-shan, YAO Lan, GAO Fu-xiang, XU Jun-can |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Jisuanji kexue, Vol 49, Iss 4, Pp 362-368 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1002-137X |
| DOI: |
10.11896/jsjkx.210300032 |
| Popis: |
Electronic medical data surges along with the constant development of information technologies and medical care digitalization.It provides foundations for further application on data analysis, data mining and intelligent diagnosis.The fact that me-dical data are massive and involve a lot of patient privacy.How to protect patient privacy while using medical data is challenging.The predominant principle for the solutions is anonymity.It is not competent in confidentiality or availability when attackers possess strong background knowledge.This paper proposes an optimized classification tree and an improved Diffpart.In our design, association of data is introduced to sift set-valued data for DP based perturbation, which satisfies the utility and supports statistic query.Then test is conducted with 240000 practical medical data and the results show that the proposed algorithm holds DP distribution and outperforms Diffpart in privacy and utility. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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