A fine-grained and privacy-preserving query scheme for fog computing-enhanced location-based service

Sensors (Switzerland)

Volumn 17, Issue 7, 2017, Pages

  Yang, Xue ^a   Yin, Fan ^a   Tang, Xiaohu ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Fine grained; Fog computing; Location based services (LBS); Low latency; Privacy preserving

Indexed keywords

DATA PRIVACY; ENCODING (SYMBOLS); FOG; KNOWLEDGE BASED SYSTEMS; LOCATION; TELECOMMUNICATION SERVICES;

COMMUNICATION OVERHEADS; FINE GRAINED; LOW LATENCY; PERFORMANCE ANALYSIS; PRIVACY PRESERVATION; PRIVACY PRESERVING; RESEARCH CHALLENGES; STATE-OF-THE-ART SCHEME;

LOCATION BASED SERVICES;

ARTICLE; PRIVACY;

EID: 85023750059     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17071611     Document Type: Article

References (36)

1. Bonomi, F.; Milito, R.A.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 17 August 2012; pp. 13–16.
2. Dinh, T.; Kim, Y.; Lee, H. A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications. Sensors 2017, 17, 489.
3. Ryu, K.; Koizumi, Y.; Hasegawa, T. Name-based geographical routing/forwarding support for location-basedr IoT services. In Proceedings of the 24th IEEE International Conference on Network Protocols, Singapore, 8–11 November 2016; pp. 1–6.
4. Lu, R.; Lin, X.; Shen, X.S. SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for Mobile-Healthcare Emergency. IEEE Trans. Parallel Distrib. Syst. 2013, 24, 614–624.
5. Freeman, H.; Zhang, T. The emerging era of fog computing and networking [The President’s Page]. IEEE Commun. Mag. 2016, 54, 4–5.
6. Yannuzzi, M.; Milito, R.A.; Serral-Gracià, R.; Montero, D.; Nemirovsky, M. Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing. In Proceedings of the 19th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, Athens, Greece, 1–3 December 2014; pp. 325–329.
7. Stojmenovic, I.; Wen, S. The Fog Computing Paradigm: Scenarios and Security Issues. In Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, Warsaw, Poland, 7–10 September 2014; pp. 1–8.
8. Yi, S.; Li, C.; Li, Q. A Survey of Fog Computing: Concepts, Applications and Issues. In Proceedings of the 2015 Workshop on Mobile Big Data, Mobidata@MobiHoc 2015, Hangzhou, China, 21 June 2015; pp. 37–42.
9. Deng, R.; Lu, R.; Lai, C.; Luan, T.H.; Liang, H. Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption. IEEE Internet Things J. 2016, 3, 1171–1181.
10. Ardagna, C.A.; Cremonini, M.; Damiani, E.; di Vimercati, S.D.C.; Samarati, P. Location Privacy Protection Through Obfuscation-Based Techniques. In Proceedings of the 21st Annual IFIP WG 11.3 Working Conference on Data and Applications Security, Redondo Beach, CA, USA, 8–11 July 2007; pp. 47–60.
11. Kido, H.; Yanagisawa, Y.; Satoh, T. An anonymous communication technique using dummies for location-based services. In Proceedings of the International Conference on Pervasive Services 2005, Santorini, Greece, 11–14 July 2005; pp. 88–97.
12. Gruteser, M.; Grunwald, D. Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. In Proceedings of the First International Conference on Mobile Systems, Applications, and Services, San Francisco, CA, USA, 5–8 May 2003.
13. Mokbel, M.F.; Chow, C.; Aref, W.G. The New Casper: Query Processing for Location Services without Compromising Privacy. In Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, 12–15 September 2006; pp. 763–774.
14. Chor, B.; Kushilevitz, E.; Goldreich, O.; Sudan, M. Private Information Retrieval. J. ACM 1998, 45, 965–981.
15. Ghinita, G.; Kalnis, P.; Khoshgozaran, A.; Shahabi, C.; Tan, K. Private queries in location based services: Anonymizers are not necessary. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada, 10–12 June 2008; pp. 121–132.
16. Yi, X.; Paulet, R.; Bertino, E.; Varadharajan, V. Practical k nearest neighbor queries with location privacy. In Proceedings of the IEEE 30th International Conference on Data Engineering, Chicago, IL, USA, 31 March–4 April 2014; pp. 640–651.
17. Olumofin, F.G.; Goldberg, I. Revisiting the Computational Practicality of Private Information Retrieval. In Proceedings of the 15th International Conference on Financial Cryptography and Data Security, Gros Islet, St. Lucia, 28 February–4 March 2011; pp. 158–172.
18. Li, L.; Lu, R.; Huang, C. EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data. IEEE Internet Things J. 2016, 3, 206–218.
19. Zhu, H.; Lu, R.; Huang, C.; Chen, L.; Li, H. An Efficient Privacy-Preserving Location-Based Services Query Scheme in Outsourced Cloud. IEEE Trans. Veh. Technol. 2016, 65, 7729–7739.
20. Peng, T.; Liu, Q.; Meng, D.; Wang, G. Collaborative trajectory privacy preserving scheme in location-based services. Inf. Sci. 2017, 387, 165–179.
21. Lai, C.; Lu, R.; Zheng, D.; Li, H.; Shen, X.S. Toward secure large-scale machine-to-machine comm unications in 3GPP networks: Challenges and solutions. IEEE Commun. Mag. 2015, 53, 12–19.
22. Dastjerdi, A.V.; Buyya, R. Fog Computing: Helping the Internet of Things Realize Its Potential. IEEE Comput. 2016, 49, 112–116.
23. Huang, C.; Ma, M.; Liu, Y.; Liu, A. Preserving Source Location Privacy for Energy Harvesting WSNs. Sensors 2017, 17, 724.
24. Wang, L.; Liu, G.; Sun, L. A Secure and Privacy-Preserving Navigation Scheme Using Spatial Crowdsourcing in Fog-Based VANETs. Sensors 2017, 17, 668.
25. Boneh, D.; Franklin, M.K. Identity-Based Encryption from the Weil Pairing. In Proceedings of the 21st Annual International Cryptology Conference on Advances in Cryptology, Santa Barbara, CA, USA, 19–23 August 2001; pp. 213–229.
26. Wong, W.K.; Cheung, D.W.; Kao, B.; Mamoulis, N. Secure kNN computation on encrypted databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Providence, RI, USA, 29 June–2 July 2009; pp. 139–152.
27. Lu, R.; Heung, K.; Lashkari, A.H.; Ghorbani, A.A. A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT. IEEE Access 2017, 5, 3302–3312.
28. Yang, X.; Lu, R.; Liang, H.; Tang, X. SFPM: A Secure and Fine-Grained Privacy-Preserving Matching Protocol for Mobile Social Networking. Big Data Res. 2016, 3, 2–9.
29. Boneh, D.; Lynn, B.; Shacham, H. Short Signatures from the Weil Pairing. J. Cryptol. 2004, 17, 297–319.
30. Li, H.; Yang, Y.; Luan, T.H.; Liang, X.; Zhou, L.; Shen, X.S. Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data. IEEE Trans. Dependable Secur. Comput. 2016, 13, 312–325.
31. Cao, N.; Wang, C.; Li, M.; Ren, K.; Lou, W. Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data. IEEE Trans. Parallel Distrib. Syst. 2014, 25, 222–233.
32. Liu, L.; Tamer Özsu, M. Lp Distances. In Encyclopedia of Database Systems; Springer: New York, NY, USA, 2009; p. 1662.
33. Jiang, T.; Chen, X.; Wu, Q.; Ma, J.; Susilo, W.; Lou, W. Secure and Efficient Cloud Data Deduplication with Randomized Tag. IEEE Trans. Inf. Forensic. Sec. 2017, 12, 532–543.
34. Aazam, M.; Huh, E. Fog Computing and Smart Gateway Based Communication for Cloud of Things. In Proceedings of the 2014 International Conference on Future Internet of Things and Cloud, Barcelona, Spain, 27–29 August 2014; pp. 464–470.
35. Lynn, B. PBC Library. Available online: https://crypto.stanford.edu/pbc/thesis.html (accessed on 19 April 2017).
36. Granlund, T. The Gnu MP Bignum Library. Available online: https://gmplib.org/(accessed on 19 April 2017).