• 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