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