Security and Privacy for Health Care Applications

Transcription

1 Security and Privacy for Health Care Applications Yih-Chun Hu University of Illinois at Urbana-Champaign May 7, 203

2 Story Time Who is the adversary? NSFNet The power grid Maps provided by geni.org and jojoyek.blogspot.com

3 Honest but Curious Suppose that information providers run protocols correctly, but try to infer health information

4 Less Honest, Equally Curious

5 Sharing a Secret on a Body Prior researchers propose using electrocardiogram (ECG) to share a secret Poon et al., A Novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health, IEEE Communications Magazine 2006 Bao et al., Using the timing information of heartbeats as an entity identifier to secure body sensor network, IEEE Transactions on Information Technology in Biomedicine 2008 Venkatasubramanian et al., EKG-based key agreement in body sensor networks, IEEE INFOCOM 2008 Xu et al., IMDGuard: securing implantable medical devices with the external wearable guardian, IEEE INFOCOM 20 They generally assume ideal ECG measurement, evaluations use hospital-provided data

6 Suboptimal ECG Measurement Isopotential lines represent locations of equal electrical potential Any sensing along a line has zero information In areas where the lines are sparse, sensors will gain little information [Image from:

7 Fundamental Problems Physiological value measurements are not robust to the sensor deployment location Outsiders can use other technology to remotely measure ECG and compromise physiological value-based secret sharing Existing techniques for remote measurement include using images Instant Heart Rate & CardioGraph on itunes App Store

8 External Measurement Hemoglobin carries oxygen in your blood. Oxygen-saturated hemoglobin and oxygendepleted hemoglobin absorb different spectra. Since O 2 saturation varies with the cardiac cycle, images can give pulse timing information. [Poh et al., Non-contact, automated cardiac pulse measurements using video imaging and blind source separation, Optics Express, 200] Image credit: SarekOfVulcan, Wikipedia

9 Our Exploration We sought to explore the problem of same-body detection: Practicality of using ECG signals for secret sharing (ECG experiment) Developed a novel approach for secret sharing using the body as a channel Modeled the body channel to determine the rate at which we can share secrets [Chang et al., Body Area Network Security: Robust Key Establishment Using Human Body Channel, HealthSec 202]

10 ECG Experiment Correlation coefficient () for a quantitative measurement of similarity between ideal (I) and other locations The measurements are sensitive to electrode orientation; we only show the maximum correlation (the minimum is 0) Measurements are time synchronized due to low autocorrelation (=0.0394) Abdomen ancreas Wrist rist W N B P M2 M Necklace uscle (E G) Muscle Designated target sensor locations that emulate predicted BAN use (see diagram on the right) nkle Ankle A Muscle uscle (E G)

11 ECG Experiment Result N W M P M2 A ( in cm) B

12 Our Approach Our scheme replaces the body physiological values with an artificial electrical signal We directly attach an information-carrying electrical signal to the body around the torso Safe at low voltages; used for pain treatment Use voltage below the nerve action potential Treat the body as a communications channel between a common sender and a group of receivers Image from Yeza, Wikipedia article on TENS

13 Evaluating the Human Body Channel Ideal approach: acquire a TENS unit, attach it to a subject, measure the signal at the TENS unit and at our pre-determined body locations Unfortunately, TENS units are only available by prescription in the United States Alternative approach: characterize the channel: Measure the signal attenuation in heterogeneous and homogenous meat Measure the noise level in a human body Use C B log 2 (+S/N) [Shannon, 948] as our metric

14 Human Body Channel Model Additive noise channel model S = h S + n S is the transmitted signal, S is the distorted Noise Measurement and received on signal, living body h is channel amplitude, n is noise Path Homogeneous loss model for Meat channel Experiment amplitude Heterogeneous h(d) = α dmeat -γ, Experiment where d is distance and α, γ some constants

15 Channel Measurements We place a voltage potential across two points of our sample Modulated every 00ms, to carry data We place two electrodes at various points to measure the attenuation and reception probability Bit error rates: 0% error rate at almost every region Except one that had mean error rate of 0.6% The tissue is not alive (noise is much less than in living tissue)

16 Amplitude vs Normalized Peak-to-Peak Voltage (V) % less 0-2 Data (homogeneous) Data (mouse) Fit (homogeneous) 23x more Data (homogenous) Data (heterogeneous) Fit (homogenous) h (cm) h worst case

17 Noise Variance (σ 2 ) Measurement σ 2 = 24.3 σ 2 = 38.3 N M σ 2 = 42.4 W B P σ 2 = 239. M2 A σ 2 = 3.74 σ 2 = 6.6 (σ 2 in µv 2 )

18 Capacity of Our Scheme B (d in cm, SNR in db, R in bits per hour)

19 Application to Body Area Networks Shannon capacity > secure bits per day with maximally conservative assumptions Transmit 0 symbols/second * 7200 seconds BAN applications are characterized by high-risk, low-occurrence events Secret updates are necessary only when the population of nodes changes Our scheme requires minimal hardware (two electrodes, ADC, and DAC) and is power-efficient, making it suitable for BAN applications

20 Questions??