2. Related Works LOFConclusion Introduction Contents ICISS 20142

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4.  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? Introduction ICISS 20144

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6. SIL Training-Free Localization SSID list KG MECH Branch Reliance Trends NMDC Head Office URL list www.kgmech.com/ www.tiendeo.in/Shops/hyderaba d/reliance-trends www.nmdc.co.in/ 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 … Search Engine query SSID Scanning Geo-Info Retrieving Address Processing component 10-3-310/1 Masab Tank, Hyderabad, 500028 Masab Tank Road ICISS 20146

7. SIL Framework Address Processing Evaluate & Rank Addresses Geo-Info Retrieving Search Engine Crawl Webs & Retrieve Geo-Info. SSID Scanning Scan APs Pre-process APs SSID SCANNING GEO-INFO RETRIEVING ADDRESS PROCESSING ICISS 20147

8.  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) SIL Overview Result ICISS 20148

9.  Ask third-party for geo-info:  Location privacy threat  Leakage of user location information while asking for geo-information through the cloud (third-parties, …) Geo-Info Third-Party Geo-Info SIL User Location device User SSID set SIL Problem ??? ICISS 20149

10. LOCATION OBFUSCATION FRAMEWORK ICISS 201410

11.  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 LOF Approach Preserve the location anonymity Keeping the bandwidth cost at acceptable level Preserve the location anonymity Keeping the bandwidth cost at acceptable level ICISS 201411

12.  Idea:  Add K-1 users’ info to disguise actual user’s info  Trusted anonymizer LOF K-Anonymity  Apply:  No anonymizer  Add disguised SSIDs to the query sent to third-party ICISS 201412

13. LOF Approach original set request set disguised set PIH K-Anonymity Third-Party obfuscated set Geo-Info request set self-process set self-process set ICISS 201413

14. LOF Parameters original set request set α disguised set β  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 ICISS 201414

15. LOF Distribution of Disguised SSIDs  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. ICISS 201415

16. LOF Effect of α and β on Anonymity and Overhead  α=50% β=100%: bandwidth reduced in half  α=100% β=100%: negligible bandwidth  Anonymity in both cases is at least 90% Fixed β, error range = 500m with ID SSIDs Fixed β, error range = 500m with RD SSIDs ICISS 201416

17. LOF Effect of ID and RD distributions on Anonymity  ID is better in obfuscating data than RD due to geo-correlation attribute of CGSIL Anonymity level with fixed α, error range = 500m ICISS 201417

18. LOF Correlation of α and β  Low values of β: the anonymity is dependent upon α’s value  High values of β: the anonymity is dependent upon β’s value Hit-Rate of Third-Party Prediction with β=0% Hit-Rate of Third-Party Prediction with β=200% ICISS 201418

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20.  LOF efficiently keeps the bandwidth overhead of SIL at minimal level while offering 90% anonymity.  Parameters (α, β) are configurable: CONCLUSION αβBandwidthAnonymity 50%100%½ reduced90% 100% Negligible85% ICISS 201420

21. References 1.Truc D. 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. 2.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 3.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. 4.L. Sweeney: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems (2002) 557-570 5.Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, and Dimitris Papadias: Preventing Location- Based Identity Inference in Anonymous Spatial Queries, Vol 19, No. 12. IEEE Transactions on Knowledge and Data Engineering (12-2007) 1719-1733 6.Buğra Gedik, Ling Liu: A Customizable k-Anonymity Model for Protecting Location Privacy. ICDCS (2004) 620–629 7.Ge Zhong, Urs Hengartner: A Distributed k-Anonymity Protocol for Location Privacy. IEEE Int. Conference on Pervasive Computing and Communications (PerCom) (2009) 1-10 8.Buğra Gedik, Ling Liu: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms, Vol. 7, No. 1. IEEE Transactions on Mobile Computing (2008) 9.Aris Gkoulalas–Divanis, Panos Kalnis, Vassilios S. Verykios: Providing K–Anonymity in Location Based Services, Vol. 12, Issue 1. SIGKDD Explorations ICISS 201421

22. Q&A ICISS 201422

24. SIL SIL vs. Training-Required Localization Algorithms ICISS 201424

25. LOF Overhead Analysis  90% anonymity: α=50% and β=100% Cost: 6MB per location.  No bandwidth cost: α=100% and β=100% (anonymity is reduced by 4%) Bandwidth Overhead with a Variety of α Values ICISS 201425