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Autumn 2011 Conference November 2-3, 2011 Washington, DC AGENDA Sensor Systems for Monitoring Congestive Heart Failure: Locationbased Privacy Encodings WEDNESDAY, NOVEMBER 2, 2011 TIME TOPIC 0730 – 0845 Breakfast 0845 – 0900 Conference Welcome 0900 – 1000 Keynote Address – Virtualization of R&D Driving New Health IT Requirements Vijay Pillai (Oracle Corporation) 1000 – 1030 Break Session1: Trustworthy Health Information Systems 1030 – 1050 Regret Minimizing Audits: A Learning-Theoretic Basis for Privacy Protection 1050 – 1110 Edmund Seto, Posu Yan, Ruzena Bajcsy Declarative Privacy Policy: Finite Models and Attribute-Based Encryption Ellick Chan (Stanford University) University of California, Berkeley 1110 – 1130 Context-Aware Anomaly Detection for Electronic Medical Record Systems Arunesh Sinha (Carnegie Mellon University) Yuan Xue (Vanderbilt University) 1130 – 1150 Sensor System for Monitoring Congestive Heart Failure Patients TRUST Autumn 2011 Conference Uncovering Anomalous Usage of Medical Records via Social Network Analysis November 2, 2011 Bradley Malin (Vanderbilt University) Edmund Seto (University of California, Berkeley) 1150 – 1210 1210 – 1340 Lunch Congestive Heart Failure Inability for the heart to pump enough blood to the rest of the body. Cardiovascular disease is the #1 killer in the U.S. Approximately 5.7 million Americans have Congestive Heart Failure. Each year 670,000 Americans will be newly diagnosed with CHF. The estimated direct and indirect cost of CHF in the U.S. for 2009 is $37.2 billion. Congestive Heart Failure CHF is a chronic disease Treatable Medications Lifestyle changes (diet, smoking, physical activity, weight, etc.) Frequent monitoring (every 3-6 months w/doctor) Attention to symptoms (cough, fatigue, weight gain, swollen feet) Telemonitoring Systematic review by Louis, et al., 2003 18 observational studies and 6 randomised controlled trials Findings suggest telemonitoring benefits: Early detection of deterioration Reduce readmission rates Reduce length of hospital stay Reduce readmissions Reduced mortality Case Study: Congestive Heart Failure Mobile device GPS Accelerometer BT digital scale BT blood pressure Data sent to server at Vanderbilt Patient receives regular feedback messages Privacy Considerations • Device security (authentication, device loss, etc.) • Wireless security (eavesdropping, DoS, Phishing, etc.) • Data security (encryption, access rights, audit trails, etc.) • Privacy policies – Patients control their data – Some potential benefits to sharing their data – But, also some potential risks to sharing their data Recipe dictates permitted behavior for a client application, specifically: What sensor/video/audio data the application can access, and at what Secureresolution Communication Framework for Networked Tele-Health Applications Aaron What Bestick,output Posu Yan, Ruzena Bajcsy data streams the application is allowed to create Figure 1: A sample smartphone client collecting accelerometer data, showing the sensor interface, network interface, and recipe components of the client library. Defining Contextual Exposure For example, doctor may be interested in: • Where is patient getting physical activity? • Where is patient having high blood pressure? • Where is patient having lunch? Elaboration on contextual exposure Problem: Where is patient getting physical activity? • “Physical activity” defined by p(t) (e.g., physical activity obtained from accelerometry) • “Where” defined by x(t) (e.g., location obtained from GPS) • Hence: x(t) for all t when p(t)>threshold intensity of activity • Furthermore: g(x(t)) = places (e.g., parks, schools, home, etc.) • and… Σ g(x(t)) / T (i.e., proportion of monitoring period that exposure occurred) Privacy of Inferred Context • Location of home, work, etc. Introduce random error Aggregation (1 km) Aggregation (2 km) Model of the patient What might influence a patient’s encoding decisions? • Risk adversity (cost) – Less data shared, the lower the privacy risk – Factors in various aspects of “trust” (of their physician, the network, data security, laws, etc.) • Possible reward – Sharing more data, might lead to better care • … and obviously, these vary between individuals Model of the doctor What might influence a doctor’s perspective on encoded data? • Generally more detailed data is better than less • Up to a point (saturation) • … and presumably, less variation between doctors (e.g., standard treatment protocols) Privacy in the Federal Health IT Plan: a Game Theoretic Approach Daniel Aranki, Ruzena Bajcsy What is the optimal “move” of the device? Future work • Finish implementation of the recipe architecture, including the collaboration server • User studies to define useful encodings • User studies to define utility functions • Analyze (and optimize) the patients’ decisions by extending this framework to consider various privacy and security threats. THANKS! Autumn 2011 Conference November 2-3, 2011 Washington, DC