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A N ovel Framework for LBS Privacy Preserving in Dynamic Context Environment. Le Nguyen Duy Vu Nguyen Le Vinh Nguyen Ngoc Tuan Do Son Thanh Tran Trung Hien Dang Tran Khanh. ACOMP 2011. Outline. Location-based services: privacy concerns in dynamic-context environment - PowerPoint PPT Presentation
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A NOVEL FRAMEWORKFOR LBS PRIVACY PRESERVINGIN DYNAMIC CONTEXT ENVIRONMENT
ACOMP 2011
Le Nguyen Duy VuNguyen Le VinhNguyen Ngoc TuanDo Son ThanhTran Trung HienDang Tran Khanh
Outline2
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Outline3
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Location-based service: Definition [1]4
In an abstract way
A certain service that is offered to the users based on their locations
Location-based service: Everywhere5
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.
Privacy concenrns in LBS6
“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…!!!!
Location-based service: Now7
Steadly growing with variety of services
Location-based service: Now8
Location-based service: Now9
Context-enabling flourishes the quality of LBS
Location-based service becoming context-aware service [2] 10
Key Problem11
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
Outline12
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Motivation and Approach13
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
Motivation and Approach (cont.)14
Approach: employ existing privacy
preserving algorithms evaluate privacy results modify the outputs (if
necessary)
Privacy Algorithm
Result
Evaluating
Refining
Output
Privacy algorithms [3, 4]15
Location obfuscation ie. Location pertubation
Privacy algorithms (cont.)16
Location k-anonymity
10-anonymity
Attack and Defense Models [5, 6]17
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
Related systems (1/4)18
An index-based privacy-preserving service-trigger by Y. Lee, O. Kwon [7]
Related systems (2/4)19
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
Related systems (3/4)20
CARE Middleware [8]
Related systems (4/4)21
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
Outline22
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Architecture overview23
The proposed framework24
Context Aggregation25
Context data collected from Profile Managers automatically and up to date.
Capable of solving conflicts between policies of user, service provider and context provider.
Case-based calculation26
Checking reciprocity property
Ontology Reasoner27
Checking memorization and m-invariance properties Connect to Profile Managers & retrieve relevant data
Outline28
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Demo29
Outline30
Location-based services: privacy concerns in dynamic-context environment
Privacy preserving based on an evaluating system The proposed framework Demo Conclusion
Conclusion31
Modern privacy techniques need to concern context information
A novel framework proposed to address user’s privacy in dynamic context
32
Thank you!!
References33
[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.
