PrivLBS: uma Abordagem para Preservação de Privacidade de Dados em Serviços baseados em Localização
Abstract
Location based services have been increasingly integrated into people’s daily activities. However, some of these services may not be trustworthy and lead to serious privacy breaches. This work proposes a new technique for privacy preserving data, named PrivLBS, which ensures that individual’s location will not be easily re-identified by malicious services. Experimental results show that, for euclidean distance-based attacks, individual’s probability of location re-identification, after using PrivLBS, is around 11.4%, whereas in existing work, this probability reaches 59.2%.
Keywords:
Privacy, Euclidean distance, location-based services
References
Gedik, B. and Liu, L. (2008). Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Transactions on Mobile Computing, 7(1):1–18.
Kido, H., Yanagisawa, Y., and Satoh, T. (2005). An anonymous communication technique using dummies for location-based services. In ICPS ’05. Proceedings. International Conference on Pervasive Services, 2005., pages 88–97.
Li, H., Sun, L., Zhu, H., Lu, X., and Cheng, X. (2014). Achieving privacy preservation in wifi fingerprint-based localization. In INFOCOM, 2014 Proceedings IEEE, pages 2337–2345. IEEE.
Lu, R., Lin, X., Shi, Z., and Shao, J. (2014). Plam: A privacy-preserving framework for local-area mobile social networks. In INFOCOM, 2014 Proceedings IEEE, pages 763–771. IEEE.
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. (2006). l-diversity: Privacy beyond k-anonymity. pages 24–24.
Niu, B., Gao, S., Li, F., Li, H., and Lu, Z. (2016). Protection of location privacy in continuous lbss against adversaries with background information. In 2016 International Conference on Computing, Networking and Communications (ICNC), pages 1–6.
Niu, B., Li, Q., Zhu, X., Cao, G., and Li, H. (2014). Achieving k-anonymity in privacyaware location-based services. In INFOCOM, 2014 Proceedings IEEE, pages 754–762. IEEE.
Niu, B., Li, Q., Zhu, X., Cao, G., and Li, H. (2015). Enhancing privacy through caching in location-based services. In Computer Communications (INFOCOM), 2015 IEEE Conference on, pages 1017–1025. IEEE.
Serjantov, A. and Danezis, G. (2003). Towards an information theoretic metric for anonymity. In Dingledine, R. and Syverson, P., editors, Privacy Enhancing Technologies, pages 41–53, Berlin, Heidelberg. Springer Berlin Heidelberg.
Sun, G., Chang, V., Ramachandran, M., Sun, Z., Li, G., Yu, H., and Liao, D. (2017a). Efficient location privacy algorithm for internet of things (iot) services and applications. Journal of Network and Computer Applications, 89:3 – 13. Emerging Services for Internet of Things (IoT).
Sun, G., Liao, D., Li, H., Yu, H., and Chang, V. (2017b). L2p2: A location-label based approach for privacy preserving in lbs. Future Generation Computer Systems, 74:375 – 384.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557–570.
Tsoukaneri, G., Theodorakopoulos, G., Leather, H., and Marina, M. K. (2016). On the inference of user paths from anonymized mobility data. In 2016 IEEE European Symposium on Security and Privacy (EuroS P), pages 199–213.
Ullah, I. and Shah, M. A. (2016). A novel model for preserving location privacy in internet of things. In 2016 22nd International Conference on Automation and Computing (ICAC), pages 542–547.
Vu, K., Zheng, R., and Gao, J. (2012). Efficient algorithms for k-anonymous location privacy in participatory sensing. In 2012 Proceedings IEEE INFOCOM, pages 2399–2407.
Ying, B. and Makrakis, D. (2014). Protecting location privacy with clustering anonymization in vehicular networks. In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pages 305–310. IEEE.
Zhu, X., Chi, H., Niu, B., Zhang, W., Li, Z., and Li, H. (2013). Mobicache: When k-anonymity meets cache. In Global Communications Conference (GLOBECOM), 2013 IEEE, pages 820-825. IEEE
Kido, H., Yanagisawa, Y., and Satoh, T. (2005). An anonymous communication technique using dummies for location-based services. In ICPS ’05. Proceedings. International Conference on Pervasive Services, 2005., pages 88–97.
Li, H., Sun, L., Zhu, H., Lu, X., and Cheng, X. (2014). Achieving privacy preservation in wifi fingerprint-based localization. In INFOCOM, 2014 Proceedings IEEE, pages 2337–2345. IEEE.
Lu, R., Lin, X., Shi, Z., and Shao, J. (2014). Plam: A privacy-preserving framework for local-area mobile social networks. In INFOCOM, 2014 Proceedings IEEE, pages 763–771. IEEE.
Machanavajjhala, A., Gehrke, J., Kifer, D., and Venkitasubramaniam, M. (2006). l-diversity: Privacy beyond k-anonymity. pages 24–24.
Niu, B., Gao, S., Li, F., Li, H., and Lu, Z. (2016). Protection of location privacy in continuous lbss against adversaries with background information. In 2016 International Conference on Computing, Networking and Communications (ICNC), pages 1–6.
Niu, B., Li, Q., Zhu, X., Cao, G., and Li, H. (2014). Achieving k-anonymity in privacyaware location-based services. In INFOCOM, 2014 Proceedings IEEE, pages 754–762. IEEE.
Niu, B., Li, Q., Zhu, X., Cao, G., and Li, H. (2015). Enhancing privacy through caching in location-based services. In Computer Communications (INFOCOM), 2015 IEEE Conference on, pages 1017–1025. IEEE.
Serjantov, A. and Danezis, G. (2003). Towards an information theoretic metric for anonymity. In Dingledine, R. and Syverson, P., editors, Privacy Enhancing Technologies, pages 41–53, Berlin, Heidelberg. Springer Berlin Heidelberg.
Sun, G., Chang, V., Ramachandran, M., Sun, Z., Li, G., Yu, H., and Liao, D. (2017a). Efficient location privacy algorithm for internet of things (iot) services and applications. Journal of Network and Computer Applications, 89:3 – 13. Emerging Services for Internet of Things (IoT).
Sun, G., Liao, D., Li, H., Yu, H., and Chang, V. (2017b). L2p2: A location-label based approach for privacy preserving in lbs. Future Generation Computer Systems, 74:375 – 384.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05):557–570.
Tsoukaneri, G., Theodorakopoulos, G., Leather, H., and Marina, M. K. (2016). On the inference of user paths from anonymized mobility data. In 2016 IEEE European Symposium on Security and Privacy (EuroS P), pages 199–213.
Ullah, I. and Shah, M. A. (2016). A novel model for preserving location privacy in internet of things. In 2016 22nd International Conference on Automation and Computing (ICAC), pages 542–547.
Vu, K., Zheng, R., and Gao, J. (2012). Efficient algorithms for k-anonymous location privacy in participatory sensing. In 2012 Proceedings IEEE INFOCOM, pages 2399–2407.
Ying, B. and Makrakis, D. (2014). Protecting location privacy with clustering anonymization in vehicular networks. In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pages 305–310. IEEE.
Zhu, X., Chi, H., Niu, B., Zhang, W., Li, Z., and Li, H. (2013). Mobicache: When k-anonymity meets cache. In Global Communications Conference (GLOBECOM), 2013 IEEE, pages 820-825. IEEE
Published
2018-08-25
How to Cite
D. NETO, Eduardo R.; MENDONÇA, Andre L. C.; BRITO, Felipe T.; MACHADO, Javam C..
PrivLBS: uma Abordagem para Preservação de Privacidade de Dados em Serviços baseados em Localização. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 33. , 2018, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 109-120.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2018.22223.
