Transcript A Novel Framework for LBS Privacy Preservation in Dynamic

A Novel Framework
for LBS Privacy Preservation
in Dynamic Context Environment
ACOMP 2011
Ouline
 Privacy Concern Location-based Services in
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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
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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.
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Location-based service: Everybody
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 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
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Location-based service
becoming context-aware service
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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
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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
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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
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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
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Location privacy algorithms
 Location obfuscation
 ie. Location pertubation
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Location privacy algorithms
 Location k-anonymity
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10-anonymity
Model for LBS algorithm evaluating
 Attack models categorized on adversary background
knowledge
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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
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Related works
 An index-based privacy preserving service trigger by
Y. Lee, O.Kwon
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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
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Related works
 CARE Middleware
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Related works
 CARE Middleware
 Advantages
 Manage context effeciently and dynamically
 Results can be used directly for privacy algorithm
 Scalability
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Middleware as base architecture
Location-based Database
Server
LBS Middleware
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Privacy-aware
Query Processor
Third trusted party that is
responsible on blurring the
exact location information.
Middleware as base architecture
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The proposed framework
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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.
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Context Aggregation
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Case based calculation
 Checking reciprocity property
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Case based calculation
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Ontology Reasoner
 Checking memorization and m-inVariance properties
 Connect to Profile Managers & retrieve in-the-need
data
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Ontology Reasoner
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End slide
 ... ? ! ^^  O.o !!!
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