Abstract
Location-Based Recommendation Services (LBRS) has seen an unprecedented rise in its usage in recent years. LBRS facilitates a user by recommending services based on his location and past preferences. However, leveraging such services comes at a cost of compromising one’s sensitive information like their shop** preferences, lodging places, food habits, recently visited places, etc. to the third-party servers. Losing such information could be crucial and threatens one’s privacy. Nowadays, the privacy-aware society seeks solutions that can provide such services, with minimized risks. Recently, a few privacy-preserving recommendation services have been proposed that exploit the fully homomorphic encryption (FHE) properties to address the issue. Though, it reduced privacy risks but suffered from heavy computational overheads that ruled out their commercial applications. Here, we propose SHELBRS, a lightweight LBRS that is based on switchable homomorphic encryption (SHE), which will benefit the users as well as the service providers. A SHE exploits both the additive as well as the multiplicative homomorphic properties but with comparatively much lesser processing time as it’s FHE counterpart. We evaluate the performance of our proposed scheme with the other state-of-the-art approaches without compromising security.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Badsha, S., Yi, X., Khalil, I.: A practical privacy-preserving recommender system. Data Sci. Eng. 1(3), 161–177 (2016)
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrap**. ACM Trans. Comput. Theory (TOCT) 6(3), 1–36 (2014)
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, pp. 169–178 (2009)
Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42 (2003)
Horowitz, D., Contreras, D., Salamó, M.: EventAware: a mobile recommender system for events. Pattern Recogn. Lett. 105, 121–134 (2018)
Lien, I.-T., Lin, Y.-H., Shieh, J.-R., Wu, J.-L.: A novel privacy preserving location-based service protocol with secret circular shift for k-NN search. IEEE Trans. Inf. Forensics Secur. 8(6), 863–873 (2013)
Lim, H.W., Tople, S., Saxena, P., Chang, E.-C.: Faster secure arithmetic computation using switchable homomorphic encryption. IACR Cryptol. ePrint Arch., 2014:539 (2014)
Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)
Lyu, Q., Ishimaki, Y., Yamana, H.: Privacy-preserving recommendation for location-based services. In: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), pp. 98–105. IEEE (2019)
Melchor, C.A., Gaborit, P.: A fast private information retrieval protocol. In: 2008 IEEE International Symposium on Information Theory, pp. 1848–1852. IEEE (2008)
Moon, B., Jagadish, H.V., Faloutsos, C., Saltz, J.H.: Analysis of the clustering properties of the Hilbert space-filling curve. IEEE Trans. Knowl. Data Eng. 13(1), 124–141 (2001)
Papakyriakopoulos, O., Serrano, J.C.M., Hegelich, S.: Political communication on social media: a tale of hyperactive users and bias in recommender systems. Online Soc. Netw. Media 15, 100058 (2020)
**ley, A., Wei, Yu., Zhang, N., **nwen, F., Zhao, W.: A context-aware scheme for privacy-preserving location-based services. Comput. Netw. 56(11), 2551–2568 (2012)
Qi, L., Wang, R., Chunhua, H., Li, S., He, Q., **aolong, X.: Time-aware distributed service recommendation with privacy-preservation. Inf. Sci. 480, 354–364 (2019)
Fenwick, M.I.R., Hittle, M., White, O.: Fitness app strava lights up staff at military bases. BBC Journal Archive (2018). https://www.bbc.com/news/technology-42853072
Sagan, H.: Space-Filling Curves. Springer, Heidelberg (2012)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Sun, G., Song, L., Liao, D., Hongfang, Yu., Chang, V.: Towards privacy preservation for “check-in’’ services in location-based social networks. Inf. Sci. 481, 616–634 (2019)
Utsunomiya, Y., Toyoda, K., Sasase, I.: LPCQP: lightweight private circular query protocol for privacy-preserving k-NN search. In 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pp. 59–64. IEEE (2015)
Zhang, X., Yu, L., Wang, M., Gao, W.: FM-based: algorithm research on rural tourism recommendation combining seasonal and distribution features. Pattern Recogn. Lett. (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Jain, M., Singh, P., Raman, B. (2022). SHELBRS: Location-Based Recommendation Services Using Switchable Homomorphic Encryption. In: Batina, L., Picek, S., Mondal, M. (eds) Security, Privacy, and Applied Cryptography Engineering. SPACE 2021. Lecture Notes in Computer Science(), vol 13162. Springer, Cham. https://doi.org/10.1007/978-3-030-95085-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-95085-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95084-2
Online ISBN: 978-3-030-95085-9
eBook Packages: Computer ScienceComputer Science (R0)