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ACOMP 2011 A Novel Framework for LBS Privacy Preservation in Dynamic Context Environment.

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Presentation on theme: "ACOMP 2011 A Novel Framework for LBS Privacy Preservation in Dynamic Context Environment."— Presentation transcript:

1 ACOMP 2011 A Novel Framework for LBS Privacy Preservation in Dynamic Context Environment

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 2

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

4 Location-based service: Everywhere 4 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 5

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

7 Location-based service becoming context-aware service 7

8 Privacy concerns in LBS 8 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.) 9 “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 YOU ARE TRACKED…!!!!

10 Key Problem 10 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 11 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) Privacy algorithm Evaluating Refining

12 Location privacy algorithms 12 Location obfuscation ie. Location pertubation

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

14 Model for LBS algorithm evaluating 14 Attack model s 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-in v ariance

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

16 Related works 16 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 17 CARE Middleware

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

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

20 Middleware as base architecture 20

21 The proposed framework 21

22 Context Aggregation 22 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 23

24 Case based calculation 24 Checking reciprocity property

25 Case based calculation 25

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

27 Ontology Reasoner 27

28 End slide 28... ? ! ^^  O.o !!!


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