LOF: Location Obfuscation Framework for Training-Free Localization

1. LOF • Location Obfuscation Framework for Training-Free Localization _______________ Thong m. doan Han n. dinh Nam t. nguyen Phuoc T. Tran

2. Contents Related Works Introduction LOF Conclusion ICISS 2014

3. INTRODUCTION ICISS 2014

4. Introduction ICISS 2014 • LOF is a security framework protecting privacy for SIL and other training-free localization algorithms. • SIL: Search-based Indoor Localization • Training-free: no need pre-built map for localization  save resources (human labor, time, money) • Why SIL needs protection?

5. RELATED WORKS ICISS 2014

6. SILTraining-Free Localization • Potential address list • Khanij Bhavan, Masab Tank, Hyderabad – 500028 • 10-3-310/1, Masab Tank, Mehdipatnam, Hyderabad – 500028 • 1-10-39 to 44, Begumpet, Hyderabad, AP-50001610-4/A/12/1 Masab Tank, Hyderabad – 500018 • … Masab Tank Road Address Processing component SSID Scanning Geo-Info Retrieving 10-3-310/1 MasabTank, Hyderabad, 500028 URL list www.kgmech.com/ www.tiendeo.in/Shops/hyderabad/reliance-trends www.nmdc.co.in/ SSID list KG MECH Branch Reliance Trends NMDC Head Office Search Engine query ICISS 2014

7. SILFramework GEO-INFO RETRIEVING SSID SCANNING • Scan APs • Pre-process APs SSID Scanning ADDRESS PROCESSING • Search Engine • Crawl Webs & Retrieve Geo-Info. Geo-Info Retrieving Address Processing • Evaluate & Rank Addresses ICISS 2014

8. SILOverview Result ICISS 2014 • Accuracy: ~80% (1 km error-range) • Time response: 1 min (acceptable for indoor movement) • Bandwidth cost: ~2MB per location • Geo-Retrieving component consumes much bandwidth & time • Solution: crowd-sourcing (cloud) to share geo-info between users • Result: negligible cost (2.5KB & 1 second per location)

9. SILProblem ??? User Third-Party SSID set device User Location Geo-Info SIL Geo-Info ICISS 2014 • Ask third-party for geo-info: • Location privacy threat • Leakage of user location information while asking for geo-information through the cloud (third-parties, …)

10. LOCATION OBFUSCATION FRAMEWORK LOF ICISS 2014

11. LOFApproach Preserve the location anonymity Keeping the bandwidth cost at acceptable level ICISS 2014 • K-Anonymity: • Anonymize information • Add distortion information in the query sent to the third-party • PIH – Partial Information Hiding: • Reduce amount of actual information exposed to third-party

12. LOFK-Anonymity • Apply: • No anonymizer • Add disguised SSIDs to the query sent to third-party ICISS 2014 • Idea: • Add K-1 users’ info to disguise actual user’s info • Trusted anonymizer

13. LOFApproach K-Anonymity original set self-process set self-process set Geo-Info obfuscated set PIH disguised set request set request set Third-Party ICISS 2014

14. LOFParameters original set β request set disguised set α ICISS 2014 • α • 100%: bandwidth is negligible since the whole original set is queried • α increase  anonymity decrease • β • 200%: means disguised SSIDs are two times more than original set • β increase  anonymity increase

15. LOFDistribution of Disguised SSIDs ICISS 2014 • RD– Random Distribution:The SSIDs are scattered randomly and have no geo-relation with each other. • ID– Inter-proximate Distribution:The SSIDs are geo-correlated and in close proximity with each other.

16. LOFEffect of α and β on Anonymity and Overhead Fixed β, error range = 500mwith ID SSIDs Fixed β, error range = 500mwith RDSSIDs ICISS 2014 • α=50% β=100%: bandwidth reduced in half • α=100% β=100%: negligible bandwidth • Anonymity in both cases is at least 90%

17. LOFEffect of ID and RD distributions on Anonymity Anonymity level with fixed α, error range = 500m ICISS 2014 • ID is better in obfuscating data than RD due to geo-correlation attribute of CGSIL

18. LOFCorrelation of α and β Hit-Rate of Third-Party Prediction with β=0% Hit-Rate of Third-Party Prediction with β=200% ICISS 2014 • Low values of β: the anonymity is dependent upon α’s value • High values of β: the anonymity is dependent upon β’s value

19. CONCLUSION ICISS 2014

20. CONCLUSION ICISS 2014 • LOF efficiently keeps the bandwidth overhead of SIL at minimal level while offering 90% anonymity. • Parameters (α, β) are configurable:

21. References • TrucD. Le, Thong M. Doan, Han N. Dinh, Nam T. Nguyen, “ISIL: Instant Search-based Indoor Localization”, in Conference “CCNC 2013- Mobile Device & Platform & Applications”, The 10th Annual IEEE CCNC, Las Vegas, NV, USA, 2013. • Thong M. Doan, Han N. Dinh, Nam T. Nguyen, “CGSIL: Collaborative Geo-clustering Search-based Indoor Localization”. Accepted in the 16th IEEE International Conference on High Performance Computing and Communications (HPCC), Paris, France, 2014 • Han N. Dinh, Thong M. Doan, Nam T. Nguyen, “CGSIL: A Viable Training-Free Wi-Fi Localization”, in the Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM), Rome, Italy, 2014. • L. Sweeney: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems (2002) 557-570 • PanosKalnis, Gabriel Ghinita, KyriakosMouratidis, and DimitrisPapadias: Preventing Location-Based Identity Inference in Anonymous Spatial Queries, Vol 19, No. 12. IEEE Transactions on Knowledge and Data Engineering (12-2007) 1719-1733 • BuğraGedik, Ling Liu: A Customizable k-Anonymity Model for Protecting Location Privacy. ICDCS (2004) 620–629 • GeZhong, UrsHengartner: A Distributed k-Anonymity Protocol for Location Privacy. IEEE Int. Conference on Pervasive Computing and Communications (PerCom) (2009) 1-10 • BuğraGedik, Ling Liu: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms, Vol. 7, No. 1. IEEE Transactions on Mobile Computing (2008) • ArisGkoulalas–Divanis, PanosKalnis, Vassilios S. Verykios: Providing K–Anonymity in Location Based Services, Vol. 12, Issue 1. SIGKDD Explorations ICISS 2014

22. Q&A ICISS 2014

23. Thank You !