A N ovel Framework for LBS Privacy Preserving in Dynamic Context Environment

1. A Novel Frameworkfor LBS Privacy Preservingin Dynamic Context Environment ACOMP 2011 Le Nguyen Duy Vu Nguyen Le Vinh Nguyen Ngoc Tuan Do Son Thanh Tran Trung Hien Dang Tran Khanh

2. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

3. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

4. Location-based service: Definition [1] In an abstract way A certain service that is offered to the users based on their locations

5. Location-based service: Everywhere • Location-based traffic reports: • What is the estimated time travel to reach my destination? • Location-based store finder: • Where is my nearest fast food restaurant? • Location-based advertisement: • Send E-coupons to all customers within five miles of my store.

6. Privacy concenrns in LBS YOU ARE TRACKED…!!!! “New technologies can pinpoint your location at any time and place. They promise safety and convenience but threaten privacy and security” Cover story, IEEE Spectrum, July 2003

7. Location-based service: Now • Steadly growing with variety of services

8. Location-based service: Now

9. Location-based service: Now • Context-enabling flourishes the quality of LBS

10. Location-based service becoming context-aware service [2]

11. Key Problem • Users want to entertain LBS without revealing their sensitive-information • Service providers must provide suitable privacy techniques concerning user current context • robust enough to protect users‘ information • ensure service quality

12. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

13. Motivation and Approach • Motivation: offer the ability of privacy preserving and evaluating to service providers • Context-using LBSs raise difficulties in evaluating privacy algorithm, because: • Different services require different techniques • Choice of algorithms varies according to user’s current context

14. Motivation and Approach (cont.) • Approach: • employ existing privacy preserving algorithms • evaluate privacy results • modify the outputs (if necessary) Privacy Algorithm Result Evaluating Refining Output

15. Privacy algorithms [3, 4] • Location obfuscation • ie. Location pertubation

16. Privacy algorithms (cont.) • Location k-anonymity 10-anonymity

17. Attack and Defense Models [5, 6] • Attack models categorized on adversary background-knowledge • Attack exploting Quasi-Indentifiers • Snapshot or Historical attack • Single or Multiple-Issuer Attack • Attack exploiting Knowledge of the Defense • Value the defense by metric: • Snapshot, single-issuer, def-aware attack: • Reciprocity • Historical, single-issuer attack: • memorization (i.e. historical k-anonymity) • Mutiple issuers attack: • m-invariance

18. Related systems (1/4) • An index-based privacy-preserving service-trigger by Y. Lee, O. Kwon [7]

19. Related systems (2/4) • An index-based privacy preserving service trigger by Y. Lee, O. Kwon [7] • Advantage • Easy implementation & good performance • Disadvantages • Data mostly based on user feeling • Static context, lack of context managent method

20. Related systems (3/4) • CARE Middleware [8]

21. Related systems (4/4) • CARE Middleware [8] • Advantages • Manage context effeciently and dynamically • Results can be used directly for privacy algorithms • Scalability • Disadvantages • No mechanism to evaluate privacy techniques

22. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

23. Architecture overview

24. The proposed framework

25. Context Aggregation • Context data collected from Profile Managers automatically and up to date. • Capable of solving conflicts between policies of user, service provider and context provider.

26. Case-based calculation • Checking reciprocity property

27. Ontology Reasoner • Checking memorization and m-invariance properties • Connect to Profile Managers & retrieve relevant data

28. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

29. Demo

30. Outline • Location-based services: privacy concerns in dynamic-context environment • Privacy preserving based on an evaluating system • The proposed framework • Demo • Conclusion

31. Conclusion • Modern privacy techniques need to concern context information. • A novel framework proposed to address user’s privacy in dynamic context.

32. Thank you!!

33. References • [1] F.M. Mohamed - Privacy in Location-based Services: State-of-the-art and Research Directions, MDM (2007). • [2] A. Kupper - Location-Based Services - Fundamentals and Operation, Wiley, 2005 • [3] Preserving Anonymity in Location based Services, Technical Report B6/06 (2006). • [4] C.A. Ardagna, M. Cremonini, E. Damiani, S.D.C. Vimercati, and P. Samarati - Location-Privacy Protection through Obfuscation-based Techniques, Springer 4602 (2007) 531-552. • [5] C. Bettini, S. Mascetti, X. S. Wang, D. Freni, and S. Jajodia - Anonymity and Historical-Anonymity in Location-Based Services, Springer 5599 (2009) 1-30. • [6] R. Dewri, I. Ray, I. Ray, and D. Whitley - Query m-Invariance: Preventing Query Disclosures in Continuous Location-Based Services, MDM (2010) 95-104. • [7] Y. Lee and O. Kwon - An Index-based Privacy Preserving Service Trigger in Context-Aware Computing Environments, Expert Systems with Apps. 37(7) (2010) 5192–5200. • [8] C. Bettini, L. Pareschi, and D. Riboni - Efficient Profile Aggregation and Policy Evaluation in a Middleware for Adaptive Mobile Applications, Pervasive and Mobile Computing 4(5) (2008) 697–718.