Privacy-Aware Remote Mobile Health and Fitness Monitoring: Extending the Functionality of The Berkeley Telemonitoring Framework

Kaidi Du

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2017-36
May 8, 2017

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-36.pdf

Unregulated sensitive data which are “not legally regulated but still considered sensitive due to proprietary, ethical, or privacy considerations,” can infer regulated sensitive data like medical history “protected under federal or state regulations” [1]. For example, an individual’s unregulated respiration rate may deduce if this individual has lung diseases, considered as regulated sensitive data. To protect sensitive data, it is therefore, necessary to protect both regulated and unregulated sensitive data. We can restrict access to all sensitive data, but what can we do if we would like to remotely transmit our medical history to doctors to allow analysis? How can we know that the privacy of our data is protected during the transmission? This paper introduces an implement of a method using Java to sanitize data which reveals as little as possible sensitive data to an unauthorized party so that the risk of privacy disclosure can be reduced.

Advisor: Ruzena Bajcsy


BibTeX citation:

@mastersthesis{Du:EECS-2017-36,
    Author = {Du, Kaidi},
    Editor = {Bajcsy, Ruzena and Javey, Ali and Aranki, Daniel},
    Title = {Privacy-Aware Remote Mobile Health and Fitness Monitoring: Extending the Functionality of The Berkeley Telemonitoring Framework},
    School = {EECS Department, University of California, Berkeley},
    Year = {2017},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-36.html},
    Number = {UCB/EECS-2017-36},
    Abstract = {Unregulated sensitive data which are “not legally regulated but still considered sensitive due to proprietary, ethical, or privacy considerations,” can infer regulated sensitive data like medical history “protected under federal or state regulations” [1]. For example, an individual’s unregulated respiration rate may deduce if this individual has lung diseases, considered as regulated sensitive data. To protect sensitive data, it is therefore, necessary to protect both regulated and unregulated sensitive data. We can restrict access to all sensitive data, but what can we do if we would like to remotely transmit our medical history to doctors to allow analysis? How can we know that the privacy of our data is protected during the transmission? This paper introduces an implement of a method using Java to sanitize data which reveals as little as possible sensitive data to an unauthorized party so that the risk of privacy disclosure can be reduced.}
}

EndNote citation:

%0 Thesis
%A Du, Kaidi
%E Bajcsy, Ruzena
%E Javey, Ali
%E Aranki, Daniel
%T Privacy-Aware Remote Mobile Health and Fitness Monitoring: Extending the Functionality of The Berkeley Telemonitoring Framework
%I EECS Department, University of California, Berkeley
%D 2017
%8 May 8
%@ UCB/EECS-2017-36
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-36.html
%F Du:EECS-2017-36