DAISY Data Analysis and Information SecuritY Lab

Transcription

1 DAISY Data Analysis and Information SecuritY Lab Mobile Phone Enabled Social Community Extraction for Controlling of Disease Propagation in Healthcare Yingying (Jennifer) Chen Director of Data Analysis and Information Security Lab Graduate Director of Networked Information Systems (NIS) Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, USA Web: personal.stevens.edu/~ychen6

2 Research Interests Cyber security and privacy Secure localization systems; privacy in Location Based Services (LBS) Security in mobile cloud computing, smartphone privacy Wireless security: identity-based attacks, anti-jamming, intrusion detection Security in smart grid and cognitive radio networks Mobile healthcare and social networks Wellbeing monitoring; smart vaccine delivery User authentication Activity recognition Wireless localization and context-aware sensing Wireless localization algorithms & testbeds (using mobile phone, sensor, RFID) Personnel and asset monitoring and tracking Smartphone enabled mobile social networks Mobile Computing and vehicular applications Detect driver phone use to reduce driver distraction caused by cell phone usage Exploit cell phone signal strength to perform vehicular speed estimation 2

3 Data Analysis and Information Security Lab (DAISY): Funding National Science Foundation, Google Research, Department of Defense (DoD), Army Research Office, and Air Force Research Labs Collaborators Industry: AT&T, IBM T. J. Watson Research Center, InfoBeyond. Academia: Columbia University, Rutgers University, Robert Wood Medical School, Lehigh University, University of South Carolina. Students Postdoc researchers, Ph.D., master and undergraduate students 3

4 Publications and Awards Book: Yingying Chen, Wenyuan Xu, Wade Trappe, Yanyong Zhang, Securing Emerging Wireless Systems, ISBN: ,Springer, Book chapters: Jie Yang, Yingying Chen, Richard P. Martin, Wade Trappe and Marco Gruteser, "On the Performance of Wireless Indoor Localization Using Received Signal Strength", Position Location - Theory, Practice and Advances: A Handbook for Engineers and Academics, Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE, chapter 12, Yingying Chen, Jie Yang, Wade Trappe and Richard P. Martin, "Impact of Anchor Placement and Anchor Selection on Localization Accuracy", Position Location - Theory, Practice and Advances: A Handbook for Engineers and Academics, Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE, chapter 13, Yingying Chen, Jie Yang, Wade Trappe and Richard P. Martin, "Defending against Identity- Based Attacks in Wireless Networks", Securing Cyber-Physical Infrastructures: Foundations and Challenges, Sajal Das, Krishna Kant and Nan Zhang (eds.), chapter 4, Published over 80 journal articles and referred conference papers. National Science Foundation CAREER Award Google Faculty Research Award IEEE Outstanding Contribution Award from IEEE New Jersey Coast Section, 2005, 2006, 2007, 2008 and Best Paper Award of the ACM International Conference on Mobile Computing and Networking (ACM MobiCom)

5 Talk Outline Introduction and Motivation Framework Overview Disease Propagation Model Community and Kernel Structure Extraction Vaccine and Alert Messages Distribution Simulation Evaluation Conclusion 5

6 Introduction and Motivation Rapid deployment of sensing technology in mobile phones. The collected sensing data can be used for supporting a broad range of applications: monitoring/tracking, first responder and military applications. Few studies used mobile phone-based sensor data to address healthcare related problem. 6

7 Framework Overview Main components Disease propagation model. Community and kernel structure extraction. Vaccine and alert messages distribution. 7

8 Disease Propagation Model Four states are used grounded on the standard epidemic SIR model: Susceptible without alert; Susceptible with alert; Infective; Immunized; 8

9 Community and Kernel Structure Extraction Main components: Flow overview Dividing encounter events into multiple trace to construct contact graph and extract communities and kernel structures. Merging the extracted communities and kernel structures with existing community and kernel structures. 9

10 Mobile Device Trace Segmentation Each entry of traces includes the starting and ending time of the contact as well as IDs of the mobile devices. Assume that we have D time windows: [ T, T ],[ T, T ],...,[ T, T ] D- 1 D Divide the contact trace into multiple trace pieces according to these time windows. 10

11 Construct Contact Graphs Contact graph G=(V,E) which consists of a vertex set V and an edge set E. Each vertex denotes a person and each edge denotes two persons encounter for at least W times. We choose W w 1 for community and for kernel structure with w2>. w1 = W = w2 11

12 Community Extraction and Merging A i j Communities are extracted from contact graph by hierarchical clustering and modularity Q in time window i: Merging the communities using the community merge operation for two communities if they satisfy: 12

13 Vaccine and Alert Message Distribution Two types of messages received by users: Vaccination messages: a user should go to obtain a vaccine shot; Alert Messages: a user should take precautions as directed. The kernel structures are considered first: The top K users are selected for vaccination or alert messages; The weight of each person reflects the priority; V k 13

14 Simulation Evaluation Traces collected from smart phones: MIT traces (97 participants, 20 days) Implemented our framework in a home-grown simulator; Compare with existing techniques: Random Distribution method: server randomly chooses the users to receive the vaccination and alert messages. Encounter-based method: vaccination and alert messages are sent if sick person encounters with a susceptible person. Simulation setup: Extensive experiments on MIT trace by varying different parameters in our epidemiology infection model. 14

15 Simulation Results: Ratio of Infected Persons (1) Ratio of infected person Versus Different recovery cycle Our proposed method achieves a lower infection ratio than Random Distribution and Encounter-based methods for each recovery cycle. 15

16 Simulation Results: Ratio of Infected Persons (2) Our proposed method achieves a lower final infection ratio than other methods. 16

17 Conclusion Explored the design of a mobile phone enabled community based disease propagation rate reduction framework for the healthcare domain. Developed an online algorithm for identifying dynamic communities of different relationship levels. Designed a community-based framework to make decisions on the alert messages and vaccine shot distribution. Simulation studies show our community based disease control scheme results in lower final infection ratio than existing techniques 17

18 Thanks and Questions? 18