publication . Report . Article . 2019

A privacy-preserving framework for outsourcing location-based services to the cloud

Erman Ayday; Xiaojie Zhu; Roman Vitenberg;
Open Access English
  • Published: 01 Jan 2019
  • Publisher: Department of Informatics, University of Oslo
Abstract
Thanks to the popularity of mobile devices numerous location-based services (LBS) have emerged. While several privacy-preserving solutions for LBS have been proposed, most of these solutions do not consider the fact that LBS are typically cloud-based nowadays. Outsourcing data and computation to the cloud raises a number of significant challenges related to data confidentiality, user identity and query privacy, fine-grained access control, and query expressiveness. In this work, we propose a privacy-preserving framework for outsourcing LBS to the cloud. The framework supports multi-location queries with fine-grained access control, and search by location attribu...
Subjects
free text keywords: Electrical and Electronic Engineering, Outsourcing, business.industry, business, Mobile device, Computer security, computer.software_genre, computer, Location-based service, Semantic security, Cloud computing, Access control, Security analysis, Scalability, Computer science
58 references, page 1 of 4

[1] O. Sotamaa, “All the world's a botfighter stage: Notes on locationbased multi-user gaming.” in CGDC Conf., 2002.

[2] H. Hodson, “Google's ingress game is a gold mine for augmented reality,” New Scientist, vol. 216, 2012.

[3] M. Gruteser and D. Grunwald, “Anonymous usage of locationbased services through spatial and temporal cloaking,” in Proceedings of the 1st international conference on Mobile systems, applications and services. ACM, 2003, pp. 31-42.

[4] M. F. Mokbel, C.-Y. Chow, and W. G. Aref, “The new casper: query processing for location services without compromising privacy,” in Proceedings of the 32nd international conference on Very large data bases. VLDB Endowment, 2006, pp. 763-774.

[5] B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” IEEE Transactions on Mobile Computing, vol. 7, 2008.

[6] A. R. Beresford and F. Stajano, “Location privacy in pervasive computing,” IEEE Pervasive computing, vol. 2, 2003. [OpenAIRE]

[7] R. Shokri, G. Theodorakopoulos, C. Troncoso, J.-P. Hubaux, and J.-Y. Le Boudec, “Protecting location privacy: optimal strategy against localization attacks,” in Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 2012, pp. 617-627. [OpenAIRE]

[8] M. E. Andre´s, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geo-indistinguishability: Differential privacy for location-based systems,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. ACM, 2013, pp. 901-914. [OpenAIRE]

[9] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan, “Private queries in location based services: anonymizers are not necessary,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008, pp. 121-132. [OpenAIRE]

[10] J. Shao, R. Lu, and X. Lin, “Fine: A fine-grained privacy-preserving location-based service framework for mobile devices,” in INFOCOM, 2014.

[11] L. Li, R. Lu, and C. Huang, “Eplq: Efficient privacy-preserving location-based query over outsourced encrypted data,” IEEE Internet of Things Journal, vol. 3, no. 2, pp. 206-218, 2016.

[12] M. harkey. (2016) Foursquare + uber! [Online]. Available: https:// medium.com/foursquare-direct/foursquare-uber-4e24dc7f829d

[13] S. Kim, K. Lewi, A. Mandal, H. Montgomery, A. Roy, and D. J. Wu, “Function-hiding inner product encryption is practical,” Cryptology ePrint Archive, Report 2016/440, 2016. http://eprint. iacr. org, Tech. Rep., 2016.

[14] OpenStreetMap contributors. (2017) Planet dump retrieved from https://planet.osm.org. [Online]. Available: https://www. openstreetmap.org

[15] J. Krumm, “A survey of computational location privacy,” Personal and Ubiquitous Computing, vol. 13, no. 6, pp. 391-399, 2009.

58 references, page 1 of 4
Abstract
Thanks to the popularity of mobile devices numerous location-based services (LBS) have emerged. While several privacy-preserving solutions for LBS have been proposed, most of these solutions do not consider the fact that LBS are typically cloud-based nowadays. Outsourcing data and computation to the cloud raises a number of significant challenges related to data confidentiality, user identity and query privacy, fine-grained access control, and query expressiveness. In this work, we propose a privacy-preserving framework for outsourcing LBS to the cloud. The framework supports multi-location queries with fine-grained access control, and search by location attribu...
Subjects
free text keywords: Electrical and Electronic Engineering, Outsourcing, business.industry, business, Mobile device, Computer security, computer.software_genre, computer, Location-based service, Semantic security, Cloud computing, Access control, Security analysis, Scalability, Computer science
58 references, page 1 of 4

[1] O. Sotamaa, “All the world's a botfighter stage: Notes on locationbased multi-user gaming.” in CGDC Conf., 2002.

[2] H. Hodson, “Google's ingress game is a gold mine for augmented reality,” New Scientist, vol. 216, 2012.

[3] M. Gruteser and D. Grunwald, “Anonymous usage of locationbased services through spatial and temporal cloaking,” in Proceedings of the 1st international conference on Mobile systems, applications and services. ACM, 2003, pp. 31-42.

[4] M. F. Mokbel, C.-Y. Chow, and W. G. Aref, “The new casper: query processing for location services without compromising privacy,” in Proceedings of the 32nd international conference on Very large data bases. VLDB Endowment, 2006, pp. 763-774.

[5] B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” IEEE Transactions on Mobile Computing, vol. 7, 2008.

[6] A. R. Beresford and F. Stajano, “Location privacy in pervasive computing,” IEEE Pervasive computing, vol. 2, 2003. [OpenAIRE]

[7] R. Shokri, G. Theodorakopoulos, C. Troncoso, J.-P. Hubaux, and J.-Y. Le Boudec, “Protecting location privacy: optimal strategy against localization attacks,” in Proceedings of the 2012 ACM conference on Computer and communications security. ACM, 2012, pp. 617-627. [OpenAIRE]

[8] M. E. Andre´s, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geo-indistinguishability: Differential privacy for location-based systems,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. ACM, 2013, pp. 901-914. [OpenAIRE]

[9] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan, “Private queries in location based services: anonymizers are not necessary,” in Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008, pp. 121-132. [OpenAIRE]

[10] J. Shao, R. Lu, and X. Lin, “Fine: A fine-grained privacy-preserving location-based service framework for mobile devices,” in INFOCOM, 2014.

[11] L. Li, R. Lu, and C. Huang, “Eplq: Efficient privacy-preserving location-based query over outsourced encrypted data,” IEEE Internet of Things Journal, vol. 3, no. 2, pp. 206-218, 2016.

[12] M. harkey. (2016) Foursquare + uber! [Online]. Available: https:// medium.com/foursquare-direct/foursquare-uber-4e24dc7f829d

[13] S. Kim, K. Lewi, A. Mandal, H. Montgomery, A. Roy, and D. J. Wu, “Function-hiding inner product encryption is practical,” Cryptology ePrint Archive, Report 2016/440, 2016. http://eprint. iacr. org, Tech. Rep., 2016.

[14] OpenStreetMap contributors. (2017) Planet dump retrieved from https://planet.osm.org. [Online]. Available: https://www. openstreetmap.org

[15] J. Krumm, “A survey of computational location privacy,” Personal and Ubiquitous Computing, vol. 13, no. 6, pp. 391-399, 2009.

58 references, page 1 of 4
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