Abstract
Today’s mobile and pervasive computing devices are embedded with increasingly powerful sensing capabilities that enable them to provide exceptional spatio-temporal context acquisition that is not possible with traditional static sensor networks alone. As a result, enabling these devices to share context information with one another has a great potential for enabling mobile users to exploit the nearby cyber and physical environments in participatory or human-centric computing. However, because these mobile devices are owned by and sense information about individuals, sharing the acquired context raises significant privacy concerns. In this paper, we define Magpie, which implements an alternative to existing all-or-nothing sharing solutions. Magpie integrates a decentralized context-dependent and adaptive trust scheme with a privacy preserving sharing mechanism to evaluate the risk of disclosing potentially private data. The proposed method uses this assessment to dynamically determine the sharing strategy and the quality of the context shared. Conceptually, Magpie allows devices to actively obfuscate context information so that sharing is still useful but does not breach user privacy. To our knowledge this is the first work to take both trust relationships and users’ individual privacy sensitivities into account to balance sharing and privacy preservation. We describe Magpie and then evaluate it in a series of application-oriented experiments running on the Opportunistic Network Environment (ONE) simulator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
We use “device” and “user” interchangeably because we assume that every participant is associated with a single device through which he collaborates.
- 3.
In the equation, \(a_{j,m}^{+}\) and \(a_{j,m}^{-}\) haven been replaced with \(a^{+}\) and \(a^{-}\) for simplicity.
- 4.
We omit the i as super script for variables; each step in Algorithm 1 shows the perspective of the user i who is responding to a request from peer user j.
- 5.
Code and full results at: https://github.com/liuchg/OneSim_PCSharing.git.
References
Shilton, K.: Four billion little brothers?: Privacy, mobile phones, and ubiquitous data collection. Commun. ACM 52(11), 48–53 (2009)
Eisenman, S.B., Miluzzo, E., Lane, N.D., Peterson, R.A., Ahn, G.-S., Campbell, A.T.: Bikenet: a mobile sensing system for cyclist experience mapping. ACM Trans. Sens. Netw. 6(1), 6 (2009)
Mendez, D., Perez, A.J., Labrador, N., Marron, J.J., et al.: P-sense: a participatory sensing system for air pollution monitoringand control. In: Percom Workshops, pp. 344–347 (2011)
Bales, E., Nikzad, N., Quick, N., Ziftci, C., Patrick, K., Griswold, W.: Citisense: Mobile air quality sensing for individuals and communitiesdesign and deployment of the citisense mobile air-quality system.In Proceedings of PervasiveHealth (2012)
Grim, E., Fok, C.-L., Julien, C.: Grapevine: efficient situational awareness in pervasive computingenvironments. In: Proceedings of Percom Workshops (2012)
Srivastava, M., Abdelzaher, T., Szymanski, B.: Human-centric sensing. Philos. Trans. Royal Soc. Lond. Math. Phys. Eng. Sci. 370(1958), 176–197 (2012)
Almenarez, F., Marin, A., Díaz, D., Sanchez, J.: Developing a model for trust management in pervasive devices. In: Proceedings of Percom Workshops (2006)
Wang, X., Cheng, W., Mohapatra, P., Abdelzaher, T.: Artsense: anonymous reputation and trust in participatory sensing. In: Proceedings of INFOCOM (2013)
Xiong, L., Liu, L.: Building trust in decentralized peer-to-peer electronic communities. In: Proceedings of ICECR-5 (2002)
Shi, E., Chan, T.-H., Rieffel, E.G., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: Proceedings of NDSS (2011)
Ganti, R.K., Pham, N., Tsai, Y.-E., Abdelzaher, T.F.: Poolview: stream privacy for grassroots participatory sensing. In: Proceedings of SenSys, pp. 281–294 (2008)
Dwork, C.: Differential privacy. In: Encyclopedia of Cryptography and Security, pp. 338–340 (2011)
Keränen, A., Ott, J., Kärkkäinen, T.: The one simulator for dtn protocol evaluation. In: Proceedings of SimuTOOLS, pp. 55 (2009)
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)
Pelusi, L., Passarella, A., Conti, M.: Opportunistic networking: data forwarding in disconnected mobile ad hoc networks. IEEE Commun. Mag. 44(11), 134–141 (2006)
Luhmann, N.: Familiarity, n.confidence, trust: problems and alternatives. Trust Mak. Breaking Coop. Relat. 6, 94–107 (2000)
Li, H., Singhal, M.: Trust management in distributed systems. IEEE Comput. 40(2), 45–53 (2007)
Babu, S.S., Raha, A., Naskar, M.K.: Trust evaluation based on nodes characteristics and neighbouring nodes recommendations for WSN. In: Wireless Sensor Network 2014 (2014)
Uddin, M.G., Zulkernine, M., Ahamed, S.I.: Cat: a context-aware trust model for open and dynamic systems. In: Proceedings of SAC, pp. 2024–2029 (2008)
Selcuk, A.A., Uzun, E., Pariente, M.R.: A reputation-based trust management system for p2p networks. In: Proceedings of CCGrid, pp. 251–258 (2004)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)
Bilogrevic, I., Freudiger, J., De Cristofaro, E., Uzun, E.: What’s the gist? privacy-preserving aggregation of user profiles. In: Kutyłowski, M., Vaidya, J. (eds.) ICAIS 2014, Part II. LNCS, vol. 8713, pp. 128–145. Springer, Heidelberg (2014)
Reinhardt, A., Englert, F., Christin, D.: Averting the privacy risks of smart metering by local data preprocessing. Pervasive Mob. Comput. 16, 171–183 (2015)
Pallapa, G., Das, S.K., Di Francesco, M., Aura, T.: Adaptive and context-aware privacy preservation exploiting user interactions in smart environments. Pervasive Mob. Comput. 12, 232–243 (2014)
Hengartner, U., Steenkiste, P.: Avoiding privacy violations caused by context-sensitive services. Pervasive Mob. Comput. 2(4), 427–452 (2006)
Tomasic, A., Zimmerman, J., Steinfeld, A., Huang, Y.: Motivating contribution in a participatory sensing system via quid-pro-quo. In: Proceedings of CSCW (2014)
Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M., Miu, A. Shih, E., Balakrishnan, H., Madden, S.: Cartel: a distributed mobile sensor computing system. In: Proceedings of SenSys, pp. 125–138 (2006)
Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E., Hubaux, J.: Hiding in the mobile crowd: locationprivacy through collaboration. IEEE Trans. DSC 11(3), 266–279 (2014)
Liu, Y., Rahmati, A., Huang, Y., Jang, H., Zhong, L., Zhang, Y., Zhang, S.: xshare: supporting impromptu sharing of mobile phones. In: Proceedings of MobiSys (2009)
Golrezaei, N., Molisch, A., Dimakis, A.G., Caire, G.: Femtocaching and device-to-device collaboration. IEEE Commun. Mag. 51(4), 142–149 (2013)
Oulasvirta, A.: Finding meaningful uses for context-aware technologies: thehumanistic research strategy. In: Proceedings of the SIGCHI Conference on Human Factors in ComputingSystems, pp. 247–254 (2004)
Stephen, M.: Formalising trust as a computational concept. Ph.D. dissertation. University of Stirling, Scotland (1994)
Duma, C., Shahmehri, N., Caronni, G.: Dynamic trust metrics for peer-to-peer systems. In: Proceedings of DESA, pp. 776–781 (2005)
Jiang, X., Landay, J., et al.: Modeling privacy control in context-aware systems. IEEE Pervasive Comput. 1(3), 59–63 (2002)
Lu, Y., Wang, Z., Yu, Y.-T., Fan, R., Gerla, M.: Social network based security scheme in mobile information-centric network. In: Proceedings of MED-HOC-NET (2013)
Parris, I., Bigwood, G., Henderson, T.: Privacy-enhanced social network routing in opportunistic networks. In: Proceedings of Percom Workshops, pp. 624–629 (2010)
Belyaev, Yu.K., Chepurin, E.V. (originator): Weibull distribution.http://www.encyclopediaofmath.org/index.php?title=Weibull_distribution&oldid=18906
Sarathy, R., Muralidhar, K.: Evaluating laplace noise addition to satisfy differential privacy for numeric data. Trans. Data Priv. 4(1), 1–17 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, C., Julien, C. (2015). Pervasive Context Sharing in Magpie: Adaptive Trust-Based Privacy Protection. In: Sigg, S., Nurmi, P., Salim, F. (eds) Mobile Computing, Applications, and Services. MobiCASE 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 162. Springer, Cham. https://doi.org/10.1007/978-3-319-29003-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-29003-4_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29002-7
Online ISBN: 978-3-319-29003-4
eBook Packages: Computer ScienceComputer Science (R0)