A Sensor-based Learning Public Health System

New smartphone technologies for the first time provide a platform for a new type of on-person, public health data collection and also a new type of informational public health intervention. In such interventions, it is the device via automatically collecting data relevant to the individual’s health that triggers the receipt of an informational public health intervention relevant to that individual. This will enable far more targeted and personalized public health interventions than previously possible. However, furthermore, sensorbased public health data collection, combined with such informational public health interventions provides the underlying platform for a novel and powerful new form of learning public health system. In this paper we provide an architecture for such a sensor-based learning public health system, in particular one which maintains the anonymity of its individual participants, we describe its algorithm for iterative public health intervention improvement, and examine and provide an evaluation of its anonymity maintaining characteristics.

[1]  Robert Steele,et al.  HealthPass: Fine-Grained Access Control to Portable Personal Health Records , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[2]  Robert Steele,et al.  Smartphone‐based public health information systems: Anonymity, privacy and intervention , 2015, J. Assoc. Inf. Sci. Technol..

[3]  Karl Aberer,et al.  ExposureSense: Integrating daily activities with air quality using mobile participatory sensing , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[4]  Robert Steele,et al.  Health participatory sensing networks , 2014, Mob. Inf. Syst..

[5]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[6]  Radha Poovendran,et al.  A Survey on Mix Networks and Their Secure Applications , 2006, Proceedings of the IEEE.

[7]  Gianluca Demartini,et al.  NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data , 2013, MobiWIS.

[8]  Robert Steele,et al.  Summarized data to achieve population-wide anonymized wellness measures , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  D. Blumenthal,et al.  Achieving a Nationwide Learning Health System , 2010, Science Translational Medicine.

[10]  Minho Shin,et al.  Anonysense: privacy-aware people-centric sensing , 2008, MobiSys '08.

[11]  Raghu K. Ganti,et al.  Analysis of Data from a Taxi Cab Participatory Sensor Network , 2011, MobiQuitous.

[12]  Robert Steele,et al.  Telehealth and ubiquitous computing for bandwidth-constrained rural and remote areas , 2012, Personal and Ubiquitous Computing.

[13]  Melanie Swan,et al.  Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0 , 2012, J. Sens. Actuator Networks.

[14]  Erik P. de Vink,et al.  A Formalization of Anonymity and Onion Routing , 2004, ESORICS.

[15]  Tarek F. Abdelzaher,et al.  GreenGPS: a participatory sensing fuel-efficient maps application , 2010, MobiSys '10.

[16]  Delphine Christin.  Impenetrable obscurity vs. informed decisions: privacy solutions for Participatory Sensing , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[17]  Robert Steele,et al.  An Overview of the State of the Art of Automated Capture of Dietary Intake Information , 2015, Critical reviews in food science and nutrition.

[18]  Deborah Estrin,et al.  Personal data vaults: a locus of control for personal data streams , 2010, CoNEXT.

[19]  Robert Steele,et al.  Future Personal Health Records as a Foundation for Computational Health , 2009, ICCSA.