Estimating Eavesdropping Risk for Next Generation Implants
Implanted medical devices are expected to be wireless in near future. Wireless nature of sensing, controlling and transmission brings along different security threats. In this work, an analysis of eavesdropping risk is performed for an unencrypted data transmissions from an implanted medical device such as cardiac leadless pacemaker. This work utilizes statistical attenuation model along with measures of capacity, information rate and outage probability. Results show that eavesdropping risk depends on pathloss with shadow fading, distance and information rate (R). In addition, probability of successful eavesdropping increases if legitimate nodes transmits at lower rate. Thus, a proper tradeoff between information rate (R) and eavesdropping risk should be made. Numerical results show that at an information rate of 650 kbps, an IMD has a 5% risk of successful eavesdropping at a distance of 500 mm. This work also consider different transmission parameters like heart rate, blood pressure, ECG and EMG with their information rates and find probability of successful eavesdropping at different distances. This study provide basis for designing secure implantable cardiac leadless pacemaker with associated risks involved due to wireless nature of transmission.
This work was supported by the Marie Curie Research Grants Scheme under EU Horizon 2020 research and innovation network program, with project grant no 675353 WIBEC ITN (Wireless In-Body Environment).
- 1.Pope, A., Bouxsein, P., Manning, F.J., Hanna, K.E., et al.: Innovation and Invention in Medical Devices: Workshop Summary. National Academies Press (2001)Google Scholar
- 2.Rushanan, M., Rubin, A.D., Kune, D.F., Swanson, C.M.: Sok: security and privacy in implantable medical devices and body area network. In: 2014 IEEE Symposium on Security and Privacy (SP), pp. 524–539. IEEE (2014)Google Scholar
- 3.Times, N.Y.: Vice president news. October (2013). http://www.nytimes.com
- 4.Halperin, D., Heydt-Benjamin, T.S., Ransford, B., Clark, S.S., Defend, B., Morgan, W., Fu, K., Kohno, T., Maisel, W.H.: Pacemakers and implantable cardiac defibrillators: software radio attacks and zero-power defenses. In: IEEE Symposium on Security and Privacy, SP 2008, pp. 129–142. IEEE (2008)Google Scholar
- 6.Son, S., Lee, K., Won, D., Kim, S.: U-healthcare system protecting privacy based on cloaker. In: 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 417–423. IEEE (2010)Google Scholar
- 9.Hong, J.I., Ng, J.D., Lederer, S., Landay, J.A.: Privacy risk models for designing privacy-sensitive ubiquitous computing systems. In: Proceedings of the 5th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 91–100. ACM (2004)Google Scholar
- 10.Sayrafian-Pour, K., Yang, W.-B., Hagedorn, J., Terrill, J., Yazdandoost, K.Y.: A statistical path loss model for medical implant communication channels. In: 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 2995–2999. IEEE (2009)Google Scholar
- 11.Wang, J., Wang, Q.: Body Area Communications: Channel Modeling, Communication Systems, and EMC. Wiley (2012)Google Scholar
- 12.Tse, D., Viswanath, P.: Fundamentals of Wireless Communications (2004)Google Scholar