Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing

MobiSys 2013 - Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services

Volumn , Issue , 2013, Pages 83-95

  Parate, Abhinav ^a   Chiu, Meng Chieh ^a   Ganesan, Deepak ^a   Marlin, Benjamin M ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Continuous context sensing; Energy Accuracy Privacy optimizations; Mobile computing

Indexed keywords

ACTIVITY CLASSIFICATIONS; CONTINUOUS CONTEXT-SENSING; DYNAMIC BAYESIAN NETWORKS; PRIVATE INFORMATION; REDUCE ENERGY CONSUMPTION; SYSTEM ARCHITECTURES; SYSTEMS OPTIMIZATION; TEMPORAL SMOOTHING;

ENERGY UTILIZATION; MOBILE COMPUTING;

OPTIMIZATION;

EID: 84881178016     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2462456.2464457     Document Type: Conference Paper

References (21)

1. Java bayes. http://www.cs.cmu.edu/javabayes/.
2. L. Bao and S. S. Intille. Activity Recognition from User-Annotated Acceleration Data. Pervasive Computing, pages 1-17, 2004.
3. D. Chu, A. Deshpande, J. M. Hellerstein, and W. Hong. Approximate Data Collection in Sensor Networks using Probabilistic Models. In ICDE, page 48, 2006.
4. D. Chu, N. Lane, T. Lai, C. Pang, F. Li, X. Meng, Q. Guo, and F. Zhao. Balancing energy, latency and accuracy for mobile sensor data classification. In SenSys, pages 54-67, 2011.
5. A. Deshpande, C. Guestrin, W. Hong, and S. Madden. Exploiting correlated attributes in acquisitional query processing. In ICDE, pages 143-154, 2005.
6. A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, pages 588-599, 2004.
7. N. Friedman, K. Murphy, and S. Russell. Learning the Structure of Dynamic Probabilistic Networks. In UAI, pages 139-147, 1999.
8. M. Götz, S. Nath, and J. Gehrke. Maskit: privately releasing user context streams for personalized mobile applications. In SIGMOD, pages 289-300, 2012. ACM.
9. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. In SIGKDD Explorations, pages 10-18, 2009.
10. A. Krause and C. Guestrin. Near-optimal Nonmyopic Value of Information in Graphical Models. In UAI, pages 324-331, 2005.
11. J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford. A hybrid discriminative/generative approach for modeling human activities. In IJCAI, pages 766-772, 2005.
12. M. Li, D. Ganesan, and P. Shenoy. Presto: feedback-driven data management in sensor networks. In NSDI, page 23, 2006.
13. H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. Campbell. The jigsaw continuous sensing engine for mobile phone applications. In SenSys, pages 71-84, 2010.
14. A. Meliou, C. Guestrin, and J. M. Hellerstein. Approximating sensor network queries using in-network summaries. In IPSN, pages 229-240. IEEE, 2009.
15. S. Nath. Ace: exploiting correlation for energy-efficient and continuous context sensing. In Proceedings of the 10th international conference on Mobile systems, applications, and services, MobiSys '12, pages 29-42, New York, NY, USA, 2012. ACM.
16. G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for maximizing submodular set functions-I. Mathematical Programming, 14(1):265-294, Dec. 1978.
17. A. Parate and G. Miklau. A framework for safely publishing communication traces. In Proceedings of the 18th ACM conference on Information and knowledge management, CIKM '09, pages 1469-1472, New York, NY, USA, 2009. ACM.
18. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, USA, 1988.
19. R. A. Popa, H. Balakrishnan, and A. J. Blumberg. Vpriv: protecting privacy in location-based vehicular services. In Proceedings of the 18th conference on USENIX security symposium, SSYM'09, pages 335-350, Berkeley, CA, USA, 2009. USENIX Association.
20. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. Activity recognition from accelerometer data. In IAAI, pages 1541-1546, 2005.
21. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, second edition, 2002.