Paper Info
Title
Private Data Analytics on Biomedical Sensing Data Via Distributed Computation
Abstract
Advances in biomedical sensors and mobile communication technologies have fostered the rapid growth of mobile health (mHealth) applications in the past years. Users generate a high volume of biomedical data during health monitoring, which can be used by the mHealth server for training predictive models for disease diagnosis and treatment. However, the biomedical sensing data raise serious privacy concerns, because they reveal sensitive information such as health status and lifestyles of the sensed subjects. This paper proposes and experimentally studies a scheme that keeps the training samples private while enabling accurate construction of predictive models. We specifically consider logistic regression models which are widely used for predicting dichotomous outcomes in healthcare, and decompose the logistic regression problem into small subproblems over two types of distributed sensing data, i.e., horizontally partitioned data and vertically partitioned data. The subproblems are solved using individual private data, and thus mHealth users can keep their private data locally and only upload (encrypted) intermediate results to the mHealth server for model training. Experimental results based on real datasets show that our scheme is highly efficient and scalable to a large number of mHealth users.
Year
DOI
Venue
2016
10.1109/TCBB.2016.2515610
IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
Keywords
Field
DocType
Private data analytics,logistic regression,mobile health,predictive model training
Data mining,Data analysis,Computer science,Server,Upload,Encryption,mHealth,Artificial intelligence,Information sensitivity,Machine learning,Mobile telephony,Scalability
Journal
Volume
Issue
ISSN
PP
99
1545-5963
Citations 
PageRank 
References 
9
0.49
19
Authors
3
Name
Order
Citations
PageRank
Yanmin Gong113316.82
Yuguang Fang26982476.76
Yuanxiong Guo326721.50