nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps

verfasst von : Lakhdar Meftah, Romain Rouvoy, Isabelle Chrisment

Erschienen in: Distributed Applications and Interoperable Systems

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants’ activity and to collect insightful reports from the environment (e.g., air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with time and location stamps, which may inevitably lead to user privacy leaks by discarding sensitive information.

This paper addresses this critical limitation of the state of the art by proposing a software library that improves user privacy without compromising the overall quality of the crowdsourced datasets. We propose a decentralized approach, named Fougere, to convey data samples from user devices to third-party servers. By introducing an a priori data anonymization process, we show that Fougere defeats state-of-the-art location-based privacy attacks with little impact on the quality of crowdsourced datasets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Stunner: A Smart Phone Trace for Developing Decentralized Edge Systems

Nächstes Kapitel On the Performance of ARM TrustZone

https://github.com/m3ftah/fougere

Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of CCS 2013, pp. 901–914 (2013)

Balan, R.K., Misra, A., Lee, Y.: LiveLabs: building an in-situ real-time mobile experimentation testbed. In: ACM HotMobile (2014)

Boutsis, I., Kalogeraki, V.: Location privacy for crowdsourcing applications. In: Proceedings of UbiComp 2016 (2016)

Brouwers, N., Langendoen, K.: Pogo, a middleware for mobile phone sensing. In: Proceedings of Middleware 2012 (2012)

Cerf, S., et al.: PULP: achieving privacy and utility trade-off in user mobility data. In: Proceedings of SRDS 2017, September 2017

Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., Zeinalipour-Yazti, D.: Crowdsourcing with smartphones. IEEE Internet Comput. 16(5), 36–44 (2012)CrossRef

Chen, R., Fung, B.C.M., Mohammed, N., Desai, B.C., Wang, K.: Privacy-preserving trajectory data publishing by local suppression. Inf. Sci. 231, 83–97 (2013)CrossRef

Choi, H., Chakraborty, S., Charbiwala, Z.M., Srivastava, M.B.: SensorSafe: a framework for privacy-preserving management of personal sensory information. In: Jonker, W., Petković, M. (eds.) SDM 2011. LNCS, vol. 6933, pp. 85–100. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23556-6_6CrossRef

Chow, C.Y., Mokbel, M.F., Liu, X.: A peer-to-peer spatial cloaking algorithm for anonymous location-based service. In: Proceedings of ACM SIGSPATIAL (2006)

10.

Chow, C.Y., Mokbel, M.F., Liu, X.: Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments. GeoInformatica 15(2), 351–380 (2011)CrossRef

11.

Christin, D., Bub, D.M., Moerov, A., Kasem-Madani, S.: A distributed privacy-preserving mechanism for mobile urban sensing applications. In: Proceedings of ISSNIP 2015 (2015)

12.

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)CrossRef

13.

Cornelius, C., Kapadia, A., Kotz, D., Peebles, D., Shin, M., Triandopoulos, N.: Anonysense: privacy-aware people-centric sensing. In: Proceedings of Mobisys 2008 (2008)

14.

Das, T., Mohan, P., Padmanabhan, V.N., Ramjee, R., Sharma, A.: PRISM: platform for remote sensing using smartphones. In: Proceedings of MobiSys 2010 (2010)

15.

Fawaz, K., Shin, K.G.: Location privacy protection for smartphone users. In: Proceedings of CCS 2014. ACM (2014)

16.

Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: GEPETO: a geoprivacy-enhancing toolkit. In: Proceedings of AINA Workshops 2010 (2010)

17.

Gambs, S., Killijian, M.O., Del Prado Cortez, M.N.: Next place prediction using mobility Markov chains. In: Proceedings of MPM 2012 (2012)

18.

Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: De-anonymization attack on geolocated data. J. Comput. Syst. Sci. 80(8), 1597–1614 (2014)MathSciNetCrossRef

19.

Gao, S., Ma, J., Shi, W., Zhan, G., Sun, C.: TrPF: a trajectory privacy-preserving framework for participatory sensing. IEEE Trans. Inf. Forensics Secur. 8(6), 874–887 (2013)CrossRef

20.

Haderer, N., Rouvoy, R., Seinturier, L.: A preliminary investigation of user incentives to leverage crowdsensing activities. In: Proceedings of PerCom 2013 (2013)

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. IEEE (2010)

22.

Huang, J., et al.: MobiPerf: mobile network measurement system. Technical report, University of Michigan and Microsoft Research (2011)

23.

Kifer, D.: l-diversity: privacy beyond k -anonymity. In: Proceedings of ICDE 2006, vol. 1, no. 1, March 2006

24.

Krumm, J.: Inference attacks on location tracks. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 127–143. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72037-9_8CrossRef

25.

Lin, J., Sadeh, N., Amini, S., Lindqvist, J., Hong, J.I., Zhang, J.: Expectation and purpose: understanding users’ mental models of mobile app privacy through crowdsourcing. In: Proceeding of UbiComp 2012 (2012)

26.

Ma, C.Y., Yau, D.K., Yip, N.K., Rao, N.S.: Privacy vulnerability of published anonymous mobility traces. In: Proceedings of MobiCom 2010 (2010)

27.

Meftah, L., Gomez, M., Rouvoy, R., Chrisment, I.: ANDROFLEET: testing WiFi peer-to-peer mobile apps in the large. In: Proceedings of ASE 2017 (2017)

28.

Ninghui, L., Tiancheng, L., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: Proceedings of ICDE 2007 (2007)

29.

Peng, T., Liu, Q., Meng, D., Wang, G.: Collaborative trajectory privacy preserving scheme in location-based services. Inf. Sci. 387, 165–179 (2017)CrossRef

30.

Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: CRAWDAD dataset epfl/mobility, February 2009. https://crawdad.org/epfl/mobility/20090224. Accessed 24 Feb 2009. https://doi.org/10.15783/C7J010

31.

Prandi, C., Salomoni, P., Mirri, S.: mPASS: integrating people sensing and crowdsourcing to map urban accessibility. In: Proceedings of CCNC 2014 (2014)

32.

Shokri, R., Theodorakopoulos, G., Le Boudec, J.Y., Hubaux, J.P.: Quantifying location privacy. In: Proceedings of S&P 2011, May 2011

33.

Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E., Hubaux, J.P.: Hiding in the mobile crowd: location privacy through collaboration. IEEE Trans. Dependable Secur. Comput. 11(3), 266–279 (2014)CrossRef

34.

Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty 10(5), 557–570 (2002)MathSciNetCrossRef

35.

Terrovitis, M., Mamoulis, N.: Privacy preservation in the publication of trajectories. In: Proceedings of MDM 2008 (2008)

36.

Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. ACM SIGMOD Rec. 33(1), 50–57 (2004)CrossRef

37.

Wernke, M., Skvortsov, P., Dürr, F., Rothermel, K.: A classification of location privacy attacks and approaches. Pers. Ubiquit. Comput. 18(1) (2014). https://doi.org/10.1007/s00779-012-0633-zCrossRef

Titel: FOUGERE: User-Centric Location Privacy in Mobile Crowdsourcing Apps
verfasst von: Lakhdar Meftah
Romain Rouvoy
Isabelle Chrisment
Verlag: Springer International Publishing
Buch: Distributed Applications and Interoperable Systems
Print ISBN: 978-3-030-22495-0

Electronic ISBN: 978-3-030-22496-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-22496-7_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"