Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

With the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this letter, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Finally, we propose a privacy enhancement algorithm that mitigates the leakage of this private information.

[1]  David Kotz,et al.  Privacy in mobile technology for personal healthcare , 2012, CSUR.

[2]  Robert Simon Sherratt,et al.  SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications , 2016, eHealth 360°.

[3]  James Irvine,et al.  Privacy Implications of Wearable Health Devices , 2014, SIN.

[4]  Jean-Yves Fourniols,et al.  Smart wearable systems: Current status and future challenges , 2012, Artif. Intell. Medicine.

[5]  J. D. Janssen,et al.  Assessment of energy expenditure for physical activity using a triaxial accelerometer. , 1994, Medicine and science in sports and exercise.

[6]  Niall Twomey,et al.  The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data , 2016, ArXiv.

[7]  Niall Twomey,et al.  An RSSI-based wall prediction model for residential floor map construction , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[8]  Senem Velipasalar,et al.  Wearable Camera- and Accelerometer-Based Fall Detection on Portable Devices , 2016, IEEE Embedded Systems Letters.

[9]  Tzyy-Sheng Horng,et al.  Human gesture sensor using ambient wireless signals based on passive radar technology , 2015, 2015 IEEE MTT-S International Microwave Symposium.

[10]  Lars Kai Hansen,et al.  How efficient is estimation with missing data? , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Ge Yu,et al.  Pattern Regulator: Protecting Temporal Usage Privacy for Wireless Body Area Sensor Networks , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[12]  Thea J. M. Kooiman,et al.  Reliability and validity of ten consumer activity trackers , 2015, BMC Sports Science, Medicine and Rehabilitation.

[13]  Robert J. Piechocki,et al.  A residential maintenance-free long-term activity monitoring system for healthcare applications , 2016, EURASIP Journal on Wireless Communications and Networking.

[14]  Niall Twomey,et al.  SPHERE: A sensor platform for healthcare in a residential environment , 2017 .

[15]  Alessandro Bassi,et al.  Designing, Developing, and Facilitating Smart Cities: Urban Design to IoT Solutions , 2016 .

[16]  Paula Fikkert,et al.  Specification of the Bluetooth System , 2003 .

[17]  G. Mcnicoll World Population Ageing 1950-2050. , 2002 .

[18]  Karl Woodbridge,et al.  Activity recognition based on micro-Doppler signature with in-home Wi-Fi , 2016, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).

[19]  Kyung Sup Kwak,et al.  Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications , 2010, Journal of Medical Systems.

[20]  Raluca Marin-Perianu,et al.  Energy-Efficient Assessment of Physical Activity Level Using Duty-Cycled Accelerometer Data , 2011, ANT/MobiWIS.