A Novel Frameworkfor LBS Privacy Preservationin Dynamic Context Environment ACOMP 2011
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
Location-based service: Definition In an abstract way A certain service that is offered to the users based on their locations
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.
Location-based service: Everybody • People need GPS-equipped device to entertain LBS
Location based service: Now • Draw more and more people, business attention • Fast growing with variety of services • Context involve flourish the value added services
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
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
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
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
Location privacy algorithms • Location obfuscation • ie. Location pertubation
Location privacy algorithms • Location k-anonymity 10-anonymity
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
Related works • An index-based privacy preserving service trigger by Y. Lee, O.Kwon
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
Related works • CARE Middleware
Related works • CARE Middleware • Advantages • Manage context effeciently and dynamically • Results can be used directly for privacy algorithm • Scalability
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.
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.
Case based calculation • Checking reciprocity property
Ontology Reasoner • Checking memorization and m-inVariance properties • Connect to Profile Managers & retrieve in-the-need data
End slide • ... ? ! ^^ O.o !!!