Preserving Privacy and Ensuring Efficient Functionality with Profile in Geosocial Networks

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-21 Number-2
Year of Publication : 2015
Authors : M.Sumalatha, J.Bala Murali Krishna


M.Sumalatha, J.Bala Murali Krishna"Preserving Privacy and Ensuring Efficient Functionality with Profile in Geosocial Networks". International Journal of Computer Trends and Technology (IJCTT) V21(2):61-70, March 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. ONLINE social networks have become a significant source of personal information. In this paper, propose to take first steps toward addressing the conflict between profit and privacy in geo social networks. This paper introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations . PROFILR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of co-located users. An Android implementation shows that PROFILR is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.

1. M. Gruteser and D. Grunwald, “Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking.” in Proc. of USENIX MobiSys, 2003, pp. 31–42.
2. B. Hoh and M. Gruteser, “Protecting Location Privacy Through Path Confusion,” in Proc. of SecureComm, 2005, pp. 194–205.
3. H. Hu and D. L. Lee, “Range Nearest-Neighbor Query,” IEEE TKDE,vol. 18, no. 1, pp. 78–91, 2006.
4. P. Kamat, Y. Zhang, W. Trappe, and C. Ozturk, “Enhancing Source-Location Privacy in Sensor Network Routing,” in Proc. of ICDCS, 2005,pp. 599–608.
5. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, “Incognito: EfficientFull-Domain K-Anonymity.” in Proc. of SIGMOD, 2005, pp. 49–60.
6. A. Machanavajjhala, J. Gehrke, D. Kifer, and M.Venkitasubramaniam,“l-Diversity: Privacy Beyond k-Anonymity.” in Proc. of ICDE, 2006.
7. A. Meyerson and R. Williams, “On the Complexity of Optimal Kanonymity,”in Proc. of ACM PODS, 2004, pp. 223–228.
8. M. F. Mokbel,W. G. Aref, and I. Kamel, “Analysis of Multi-DimensionalSpace-Filling Curves,” GeoInformatica, vol. 7, no. 3, pp. 179–209, 2003.
9. M. F. Mokbel, C. Y. Chow, and W. G. Aref, “The New Casper: Query Processing for Location Services without Compromising Privacy,” in Proc. of VLDB, 2006, pp. 763–774.
10. B. Moon, H. Jagadish, and C. Faloutsos, “Analysis of the Clustering Properties of the Hilbert Space-Filling Curve,” IEEE TKDE, vol. 13, no. 1, pp. 124–141, 2001.
11. P. Samarati, “Protecting Respondents’ Identities in Microdata Release.”IEEE TKDE, vol. 13, no. 6, pp. 1010–1027, 2001.
12. H. Samet, The Design and Analysis of Spatial Data Structures.Addison-Wesley, 1990.
13. L. Sweeney, “k-Anonymity: A Model for Protecting Privacy,” Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5,pp. 557–570, 2002.
14. Y. Tao and D. Papadias, “Historical spatio-temporal aggregation,” ACMTrans. Inf. Syst., vol. 23, no. 1, pp. 61–102, 2005.
15. Y. Tao, D. Papadias, and Q. Shen, “Continuous Nearest Neighbor Search.” in Proc. of VLDB, 2002, pp. 287–298.
16. Y.Theodoridis,“TheR-tree-portal,,” 2003. [Online]. Available:
17. X. Xiao and Y. Tao, “Personalized Privacy Preservation.” in Proc. Of SIGMOD, 2006, pp. 229–240.

Social implications of technology, technology social factors, privacy.