Advertisement

Privacy, Trust and Incentives in Participatory Sensing

  • Mehdi Riahi
  • Rameez Rahman
  • Karl Aberer
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

In this chapter, we study the socioeconomic issues that can arise in distributed computing environments such as distributed and open, participatory sensing systems. Due to the decentralized nature of such systems, they present many challenges, some of which are equally socioeconomic and technical in essence. Three such major challenges arise in participatory sensing, one economic and two social. The economic problem is centered around the provision of incentives. How can participants be provided with incentives to ensure that they contribute to the system; that they provide sensed data when requested; and take part in various sensing activities?

Keywords

Mobile Node Incentive Scheme Privacy Protection Trust Platform Module Reputation Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Amintoosi, H., Kanhere, S.S.: A reputation framework for social participatory sensing systems. Mobile Netw. Appl. 19(1), 88–100 (2014)CrossRefGoogle Scholar
  2. Amintoosi, H., Kanhere, S.S.: A trust-based recruitment framework for multi-hop social participatory sensing. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 266–273 (2013)Google Scholar
  3. Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211(4489), 1390–1396 (1981)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  4. Castelluccia, C., Mykletun, E., Tsudik, G.: Efficient aggregation of encrypted data in wireless sensor networks. In: The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous 2005), pp. 109–117. IEEE, New York (2005)Google Scholar
  5. Chaum, D.: Blind signatures for untraceable payments. In: Advances in Cryptology Proceedings of Crypto 82, pp. 199–203 (1983)MathSciNetzbMATHGoogle Scholar
  6. Christin, D., Reinhardt, A., Kanhere, S.S., Hollick, M.: A survey on privacy in mobile participatory sensing applications. J. Syst. Softw. 84(11), 1928–1946 (2011)CrossRefGoogle Scholar
  7. Christin, D., Pons-Sorolla, D.R., Kanhere, S.S., Hollick, M.: Trustmeter: a trust assessment framework for collaborative path hiding in participatory sensing applications. Technical Report, Technische Universität Darmstadt (2012)Google Scholar
  8. Christin, D., Roßkopf, C., Hollick, M., Martucci, L.A., Kanhere, S.S.: Incognisense: an anonymity-preserving reputation framework for participatory sensing applications. Pervasive Mob. Comput. 9(3):353–371 (2013)CrossRefGoogle Scholar
  9. Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., Triandopoulos, N.: Anonysense: privacy-aware people-centric sensing. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, MobiSys ’08, pp. 211–224. ACM, New York (2008)Google Scholar
  10. De Cristofaro, E., Soriente, C.: Short paper: pepsi - privacy-enhanced participatory sensing infrastructure. In: Proceedings of the Fourth ACM Conference on Wireless Network Security, WISEC ’11, pp. 23–28. ACM, New York (2011)Google Scholar
  11. Deng, L., Cox, L.P.: Livecompare: grocery bargain hunting through participatory sensing. In: Proceedings of the 10th Workshop on Mobile Computing Systems and Applications, p. 4. ACM, New York (2009)Google Scholar
  12. Dimitriou, T., Krontiris, I., Sabouri, A.: Pepper: a querier’s privacy enhancing protocol for participatory sensing. In: Security and Privacy in Mobile Information and Communication Systems, vol. 107, pp. 93–106. Springer, Berlin, Heidelberg (2012)Google Scholar
  13. Dingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. In: Proceedings of the 13th Conference on USENIX Security Symposium, SSYM’04, vol. 13, pp. 21–21. USENIX Association, Berkeley, CA (2004)Google Scholar
  14. Domingo-Ferrer, J., Mateo-Sanz, J.M.: Practical data-oriented microaggregation for statistical disclosure control. IEEE Trans. Knowl. Data Eng. 14(1), 189–201 (2002)CrossRefGoogle Scholar
  15. Dua, A., Bulusu, N., Feng, W.-C., Hu, W.: Towards trustworthy participatory sensing. In: Proceedings of the 4th USENIX Conference on Hot Topics in Security, HotSec’09, pp. 8–8. USENIX Association, Berkeley, CA (2009)Google Scholar
  16. Erfani, S.M., Karunasekera, S., Leckie, C., Parampalli, U.: Privacy-preserving data aggregation in participatory sensing networks. In: 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp. 165–170 (2013)Google Scholar
  17. Ganeriwal, S., Balzano, L.K., Srivastava, M.B.: Reputation-based framework for high integrity sensor networks. ACM Trans. Sens. Netw. 4(3), 15:1–15:37, June 2008.Google Scholar
  18. Ganti, R.K. Pham, N., Tsai, Y.-E., Abdelzaher, T.F.: Poolview: stream privacy for grassroots participatory sensing. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys ’08, pp. 281–294. ACM, New York (2008)Google Scholar
  19. Gilbert, P., Cox, L.P., Jung, J., Wetherall, D.: Toward trustworthy mobile sensing. In: Proceedings of the Eleventh Workshop on Mobile Computing Systems and Applications, HotMobile ’10, pp. 31–36. ACM, New York (2010)Google Scholar
  20. He, W., Liu, X., Nguyen, H., Nahrstedt, K., Abdelzaher, T.: Pda: privacy-preserving data aggregation in wireless sensor networks. In: 26th IEEE International Conference on Computer Communications, pp. 2045–2053. IEEE, New york (2007)Google Scholar
  21. Hu, L., Shahabi, C.: Privacy assurance in mobile sensing networks: go beyond trusted servers. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 613–619 (2010)Google Scholar
  22. Huang, K.L., Kanhere, S.S., Hu, W.: Are you contributing trustworthy data?: the case for a reputation system in participatory sensing. In: Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, MSWIM ’10, pp. 14–22. ACM, New York (2010)Google Scholar
  23. Huang, K.L., Kanhere, S.S., Hu, W.: Preserving privacy in participatory sensing systems. Comput. Commun. 33(11), 1266–1280 (2010)CrossRefGoogle Scholar
  24. Huang, K.L., Kanhere, S.S., Hu, W.: A privacy-preserving reputation system for participatory sensing. In: 37th Annual IEEE Conference on Local Computer Networks, pp. 10–18 (2012)Google Scholar
  25. Jaimes, L.G., Vergara-Laurens, I., Labrador, M.A.: A location-based incentive mechanism for participatory sensing systems with budget constraints. In: 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 103–108. IEEE, New York (2012)Google Scholar
  26. Johnson, P., Kapadia, A., Kotz, D., Triandopoulos, N., Hanover, N.H.: People-centric urban sensing: security challenges for the new paradigm. Technical report, Dartmouth College, Computer Science, Hanover, NH (2007)Google Scholar
  27. Kapadia, A., Triandopoulos, N., Cornelius, C., Peebles, D., Kotz, D.: Anonysense: opportunistic and privacy-preserving context collection. In: Pervasive Computing. Lecture Notes in Computer Science, vol. 5013, pp. 280–297. Springer, Berlin, Heidelberg (2008)Google Scholar
  28. Kawasaki, H., Yamamoto, A., Kurasawa, H., Sato, H., Nakamura, M., Matsumura, H.: Top of worlds: method for improving motivation to participate in sensing services. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 594–595. ACM, New York (2012)Google Scholar
  29. Kazemi, L., Shahabi, C.: A privacy-aware framework for participatory sensing. SIGKDD Explor. Newsl. 13(1), 43–51 (2011)CrossRefGoogle Scholar
  30. Kazemi, L., Shahabi, C.: Towards preserving privacy in participatory sensing. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 328–331 (2011)Google Scholar
  31. Kazemi, L., Shahabi, C.: TAPAS: trustworthy privacy-aware participatory sensing. Knowl. Inf. Syst. 37(1), 105–128 (2013). doi:10.1007/s10115-012-0573-y. http://dx.doi.org/10.1007/s10115-012-0573-y CrossRefGoogle Scholar
  32. Kido, H., Yanagisawa, Y., Satoh, T.: An anonymous communication technique using dummies for location-based services. In: Proceedings of International Conference on Pervasive Services, ICPS ’05, pp. 88–97 (2005)Google Scholar
  33. Krause, A., Horvitz, E., Kansal, A., Zhao, F.: Toward community sensing. In: Proceedings of the 7th International Conference on Information Processing in Sensor Networks, IPSN ’08, pp. 481–492. IEEE Computer Society, Washington, DC (2008)Google Scholar
  34. Krontiris, I., Albers, A.: Monetary incentives in participatory sensing using multi-attributive auctions. Int. J. Parallel Emergent Distrib. Syst. 27(4), 317–336 (2012)CrossRefGoogle Scholar
  35. Krontiris, I., Dimitriou, T.: Privacy-respecting discovery of data providers in crowd-sensing applications. In: 9th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS’13 (2013)Google Scholar
  36. Lan, K.C., Wang, H.Y.: On providing incentives to collect road traffic information. In: International Wireless Communications and Mobile Computing Conference (IWCMC 13) (2013)Google Scholar
  37. Lee, J.S., Hoh, B.: Sell your experiences: a market mechanism based incentive for participatory sensing. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 60–68. IEEE, New York (2010)Google Scholar
  38. Li, Q., Cao, G.: Providing privacy-aware incentives for mobile sensing. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), vol. 18, p. 22 (2013)MathSciNetGoogle Scholar
  39. Lim, H.-S., Moon, Y.-S. Bertino, E.: Provenance-based trustworthiness assessment in sensor networks. In: Proceedings of the Seventh International Workshop on Data Management for Sensor Networks, DMSN ’10, pp. 2–7. ACM, New York, (2010)Google Scholar
  40. Lu, H., Jensen, C.S., Yiu, M.N.: Pad: privacy-area aware, dummy-based location privacy in mobile services. In: Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE ’08, pp. 16–23. ACM, New York (2008)Google Scholar
  41. Luo, T., Tham, C.-K.: Fairness and social welfare in incentivizing participatory sensing. In: 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 425–433. IEEE, New York (2012)Google Scholar
  42. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: Privacy Beyond K-anonymity. ACM Trans. Knowl. Discov. Data 1(1), article no. 3 (2007). doi:10.1145/1217299.1217302. http://doi.acm.org/10.1145/1217299.1217302
  43. Mawji, A., Hassanein, H.: A utility-based incentive scheme for p2p file sharing in mobile ad hoc networks. In: IEEE International Conference on Communications, ICC’08, pp. 2248–2252. IEEE, New York (2008)Google Scholar
  44. Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, E., Hansen, M., Howard, E., West, R., Boda, P.: Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, MobiSys ’09, pp. 55–68. ACM, New York, (2009)Google Scholar
  45. Pham, N., Ganti, R.K., Uddin, Y.S., Nath, S., Abdelzaher, T.: Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing. In: Proceedings of the 7th European Conference on Wireless Sensor Networks, EWSN’10, pp. 114–130. Springer, Berlin, Heidelberg (2010)Google Scholar
  46. Puttaswamy, K.P.N., Bhagwan, R., Padmanabhan, V.N.: Anonygator: privacy and integrity preserving data aggregation. In: Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware, Middleware ’10, pp. 85–106. Springer, Berlin, Heidelberg (2010)Google Scholar
  47. Reddy, S., Estrin, D., Hansen, M., Srivastava, M.: Examining micro-payments for participatory sensing data collections. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 33–36. ACM, New York (2010)Google Scholar
  48. Reddy, S., Estrin, D., Srivastava, M.: Recruitment framework for participatory sensing data collections. In: Proceedings of the 8th International Conference on Pervasive Computing, Pervasive’10, pp. 138–155. Springer, Berlin, Heidelberg (2010)Google Scholar
  49. Rodhe, I., Rohner, C., Ngai, E.C.-H.: On location privacy and quality of information in participatory sensing. In: Proceedings of the 8th ACM Symposium on QoS and Security for Wireless and Mobile Networks, Q2SWinet ’12, pp. 55–62. ACM, New York (2012)Google Scholar
  50. Schweizer, I., Meurisch, C., Gedeon, J., Bärtl, R., Mühlhäuser, M.: Noisemap: multi-tier incentive mechanisms for participative urban sensing. In: Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones, p. 9. ACM, New York (2012)Google Scholar
  51. Shi, J., Zhang, R., Liu, Y., Zhang, Y.: Prisense: privacy-preserving data aggregation in people-centric urban sensing systems. In: Proceedings of the 29th Conference on Information Communications, INFOCOM’10, pp. 758–766. IEEE, Piscataway, NJ (2010)Google Scholar
  52. Shin, M., Cornelius, C., Peebles, D., Kapadia, A., Kotz, D., Triandopoulos, N.: Anonysense: a system for anonymous opportunistic sensing. Pervasive Mob. Comput. 7(1), 16–30 (2011)CrossRefGoogle Scholar
  53. Shokri, R., Troncoso, C., Diaz, C., Freudiger, J., Hubaux, J.-P.: Unraveling an old cloak: k-anonymity for location privacy. In: Proceedings of the 9th Annual ACM Workshop on Privacy in the Electronic Society, WPES ’10, pp. 115–118. ACM, New York (2010)Google Scholar
  54. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 10(5), 557–570 (2002)Google Scholar
  55. Thepvilojanapong, N., Tsujimori, T., Wang, H., Ohta, Y., Zhao, Y., Tobe, Y.: Impact of incentive mechanism in participatory sensing environment. In: SMART 2013: The Second International Conference on Smart Systems, Devices and Technologies. IARIA, pp. 88–92 (2013). ISBN: 978-1-61208-282-0Google Scholar
  56. Wang, X., Govindan, K., Mohapatra, P.: Collusion-resilient quality of information evaluation based on information provenance. In: 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 395–403 (2011)Google Scholar
  57. Wang, X.O., Cheng, W., Mohapatra, P., Abdelzaher, T.F.: ARTSense: anonymous reputation and trust in participatory sensing. In: INFOCOM, 2013 Proceedings IEEE, pp. 2517–2525. IEEE, New York (2013). http://dblp.uni-trier.de/db/conf/infocom/infocom2013.html#WangCMA13
  58. Yang, H., Zhang, J., Roe, P.: Using reputation management in participatory sensing for data classification. Procedia Comput. Sci. 5, 190–197 (2011) The 2nd International Conference on Ambient Systems, Networks and Technologies (ANT-2011)/The 8th International Conference on Mobile Web Information Systems (MobiWIS 2011).Google Scholar
  59. You, T.-H., Peng, W.-C., Lee, W.-C.: Protecting moving trajectories with dummies. In: Proceedings of the 2007 International Conference on Mobile Data Management, MDM ’07, pp. 278–282. IEEE Computer Society, Washington, DC (2007)Google Scholar
  60. Yu, Y., Li, K., Zhou, W., Li, P.: Trust mechanisms in wireless sensor networks: attack analysis and countermeasures. J. Netw. Comput. Appl. 35(3), 867–880 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.School of Computer and Communication SciencesÉcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations