Team members

Mani Srivastava (Thrust 3 Lead, UCLA)
Santosh Kumar (Memphis)

Moustafa Alzantot (UCLA)
Rumanna Bari (Memphis)
Nazir Saleheen (Memphis)

Behavioral Privacy

Privacy and the use of smartphones and mobile sensors go hand-in-hand when developing mobile health research platforms. Smartphones have onboard sensors that can interface with a variety of external body-worn sensors, and this platform can be leveraged for continuous and unobtrusive monitoring of individuals as they go about their daily lives.

lock phone

Data collected in this manner can be shared with health care providers, who can use it to better understand how the environment influences an individual’s health and provide more proactive healthcare as a result.

But the potential for more affordable and more proactive healthcare through the use of continuously collected individual data must be paired with the concern for individual privacy. [1]

MD2K has made that an ongoing aspect of its research, and has developed mSieve, a privacy framework for sharing physiological sensor data. The mSieve framework is a new behavioral privacy metric that is based on differential privacy. It uses a novel data substitution mechanism to protect behavioral privacy expressed in terms of a whitelist and a blacklist of inferences.

mCerebrum, the software platform developed by MD2K implements privacy controls that provide a type of dynamic consent mechanisms that allow study participants to stop data collection for a while under private settings, and implements Cryptimg software that uses encryption for privacy-preserving outsourcing of image processing functions to untrusted cloud services.

[1] Nazir Saleheen; Supriyo Chakraborty; Nasir Ali; Md Mahbubur Rahman; Syed Monowar Hossain; Rummana Bari; Eugene Buder; Mani Srivastava; Santosh Kumar: mSieve: Differential Behavioral Privacy in Time Series of Mobile Sensor Data. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 706-717, ACM, New York, NY USA, 2016, ISBN: 978-1-4503-4461-6.





Copyright © 2017 MD2K. MD2K is supported by the National Institutes of Health Big Data to Knowledge Initiative (Grant #1U54EB020404)

Team: Cornell Tech, GA Tech, Harvard, U. Memphis, Northwestern, Ohio State, Open mHealth, UCLA, UCSD, UCSF, UMass, U. Michigan, Utah, WVU