DocumentCode :
1665214
Title :
Privacy Preserving Data Analysis in Mental Health Research
Author :
Jingquan Li ; Xueying Li
Author_Institution :
Sch. of Bus., Texas A&M Univ.-San Antonio, San Antonio, TX, USA
fYear :
2015
Firstpage :
95
Lastpage :
101
Abstract :
The digitalization of mental health records and psychotherapy notes has made individual mental health data more readily accessible to a wide range of users including patients, psychiatrists, researchers, statisticians, and data scientists. However, increased accessibility of highly sensitive mental records threatens the privacy and confidentiality of psychiatric patients. The objective of this study is to examine privacy concerns in mental health research and develop a privacy preserving data analysis approach to address these concerns. In this paper, we demonstrate the key inadequacies of the existing privacy protection approaches applicable to use of mental health records and psychotherapy notes in records based research. We then develop a privacy-preserving data analysis approach that enables researchers to protect the privacy of people with mental illness once granted access to mental health records. Furthermore, we choose a demonstration project to show the use of the proposed approach. This paper concludes by suggesting practical implications for mental health researchers and future research in the field of privacy-preserving data analytics.
Keywords :
data analysis; data privacy; electronic health records; patient treatment; mental health record digitalization; mental health research; privacy preserving data analysis; privacy protection; psychiatric patient confidentiality; psychiatric patient privacy; psychotherapy note digitalization; Data analysis; Data privacy; Databases; Medical diagnostic imaging; Medical services; Privacy; Security; big data; data analaysis; mental health records; mental health research; privacy; psychotherapy notes; records-based research; security;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
Type :
conf
DOI :
10.1109/BigDataCongress.2015.23
Filename :
7207207
Link To Document :
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