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A Novel Framework for LBS Privacy Preservation in Dynamic Context Environment
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A Novel Framework for LBS Privacy Preservation in Dynamic Context Environment

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  1. A Novel Frameworkfor LBS Privacy Preservationin Dynamic Context Environment ACOMP 2011

  2. Ouline • Privacy Concern Location-based Services in environment of dynamic context • A system of Privacy Preserving and Evaluating • The proposed Framework • Module evaluation and suggestions • Conclusion

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

  4. 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? • What are the restaurants within two miles of my location? • Location-based advertisement: • Send E-coupons to all customers within five miles of my store.

  5. Location-based service: Everybody • People need GPS-equipped device to entertain LBS

  6. Location based service: Now • Draw more and more people, business attention • Fast growing with variety of services • Context involve flourish the value added services

  7. Location-based service becoming context-aware service

  8. Privacy concerns in LBS • Some risk types ... • New technology promise convenience but threaten privacy and security • Enabling context in LBS make evaluating privacy techniques more complicated • Different services require different techniques • Choice of algorithms varies according to current context

  9. Privacy concenrns in LBS (cont.) 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

  10. Key Problem • Users want to entertain LBS without revealing their sensitive information • Service providers mission: • provide suitable privacy techniques concerning user current context • provide good output privacy level • robust enough to protect users‘ information • ensure service quality

  11. Approach Service Provider problem • Motivation: offer the ability of privacy preserving and evaluating to service provider • Approach: • employ existing privacy preserving algorithm • evaluate privacy result of their outputs • modify the outputs (if necessary) Evaluating Privacy algorithm Refining

  12. Location privacy algorithms • Location obfuscation • ie. Location pertubation

  13. Location privacy algorithms • Location k-anonymity 10-anonymity

  14. Model for LBS algorithm evaluating • Attack modelscategorized 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

  15. Related works • An index-based privacy preserving service trigger by Y. Lee, O.Kwon

  16. Related works • An index-based privacy preserving service trigger by Y. Lee, O. Kwon [] • Advantage • Easy implementation & good performance • Disadvantages • Data mostly based on user feeling • Static context, lack of context managent method

  17. Related works • CARE Middleware

  18. Related works • CARE Middleware • Advantages • Manage context effeciently and dynamically • Results can be used directly for privacy algorithm • Scalability

  19. Privacy-aware Query Processor Location-based DatabaseServer LBS Middleware Middleware as base architecture Third trusted party that is responsible on blurring the exact location information.

  20. Middleware as base architecture

  21. The proposed framework

  22. Context Aggregation • Context data collected from Profile Managers automatically and up to date. • Capacle of solving conflict between policies of user, service provider and others.

  23. Context Aggregation

  24. Case based calculation • Checking reciprocity property

  25. Case based calculation

  26. Ontology Reasoner • Checking memorization and m-inVariance properties • Connect to Profile Managers & retrieve in-the-need data

  27. Ontology Reasoner

  28. End slide • ... ? ! ^^  O.o !!!