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Frequent Symptom Sets Identification from Uncertain Medical Data in Differentially Private Way
Joint Authors
Qin, Zhiguang
Ding, Zhe
Qin, Zhen
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Data mining techniques are applied to identify hidden patterns in large amounts of patient data.
These patterns can assist physicians in making more accurate diagnosis.
For different physical conditions of patients, the same physiological index corresponds to a different symptom association probability for each patient.
Data mining technologies based on certain data cannot be directly applied to these patients’ data.
Patient data are sensitive data.
An adversary with sufficient background information can make use of the patterns mined from uncertain medical data to obtain the sensitive information of patients.
In this paper, a new algorithm is presented to determine the top K most frequent itemsets from uncertain medical data and to protect data privacy.
Based on traditional algorithms for mining frequent itemsets from uncertain data, our algorithm applies sparse vector algorithm and the Laplace mechanism to ensure differential privacy for the top K most frequent itemsets for uncertain medical data and the expected supports of these frequent itemsets.
We prove that our algorithm can guarantee differential privacy in theory.
Moreover, we carry out experiments with four real-world scenario datasets and two synthetic datasets.
The experimental results demonstrate the performance of our algorithm.
American Psychological Association (APA)
Ding, Zhe& Qin, Zhen& Qin, Zhiguang. 2017. Frequent Symptom Sets Identification from Uncertain Medical Data in Differentially Private Way. Scientific Programming،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1203465
Modern Language Association (MLA)
Ding, Zhe…[et al.]. Frequent Symptom Sets Identification from Uncertain Medical Data in Differentially Private Way. Scientific Programming No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1203465
American Medical Association (AMA)
Ding, Zhe& Qin, Zhen& Qin, Zhiguang. Frequent Symptom Sets Identification from Uncertain Medical Data in Differentially Private Way. Scientific Programming. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1203465
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203465