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Semanticand SensitivityAwareLocation-PrivacyProtection for the Internet of Things 
Berker Ağır, Jean-Paul Calbimonte, Karl Aberer 
Workshop on Society, Privacy and the Semantic Web -Policy and Technology 2014 
20 October2014
Introduction 
•Online Devices 
•more infiltratingin dailylife 
•online services & applications 
•They are capable of sensing 
their environment and context 
GPS 
Accelerometer 
Barometer 
Thermometer 
2
PrivacyUnder Threat 
•Honest but curious server 
•Exploits all available data 
•With limited computational power, tries to infer private information 
Background knowledge on user history 
User Events 
Process according to objectives 
Perform attack 
Observed events 
Privacy Protection Mechanism(s) 
Application Server 
3
Location Privacy 
•Location data carries highly contextual information 
•Activity tracking 
•Inferring habits 
•Physical assault 
•Rich sensor environment and continuous connectivity 
•A non-stop and unbalanced threat on privacy 
4
Common Location-Privacy Protection Approaches 
? 
Obfuscation 
Perturbation 
Hiding 
Anonymization 
Actual location 
Observed locations 
5
Shortcomings of Existing Approaches 
•Location information is multi- dimensional 
•Semantics 
•Not every location / semantic tagmight have the same importance in terms of privacy 
•Home location 
•Hospitals, restaurants 
•Overprotection 
•Service degradation 
6
Smart Adversariesand Strategies 
•Privacy has to be evaluated w.r.t. a real attack scenario 
•Adaptive protection mechanisms on user device 
•Move against each other in a strategic game 
•Location Semantics 
•User Mobility History 
•Common-knowledge sensitivities 
→Inference 
•Location Semantics 
•Adversary Modelling 
•Sensitivity Profile 
→Real-Time Adaptive Protection 
User 
Adversary 
7
Adaptive Location Privacy Protection 
8 
Adaptive Privacy Protection Mechanisms 
Privacy Estimation Module 
Estimate 
Candidate obfuscation area 
Sensitivity Profile 
Geographical& Semantic 
User History 
•Adaptive approach:Past behavior is considered before making a privacy decision 
•Causality and physical feasibility between transitions
Sensitivity Profile Configuration 
Android application allowing to set semantic and geography based sensitivity levels 
9
Adaptive Protection in Action 
10 
Lowsensitivity-university 
High sensitivity-hospital
SemanticLocation Privacy 
•Whatabout the privacyof the semantics? 
•Location mightnot matteras long as the user activityisunknown 
11 
Cinema? 
Pharmacy? 
Hotel? 
Hospital? 
Bar?
EvaluatingPrivacy 
•What is the adversary’s errorin inferring 
•users’ geographical locations? 
•the semantics of user locations? 
•How confidentis the adversary? 
•Probabilistic nature of inference 
•What is the user’s desired privacy level (i.e., sensitivity) for 
•his geographical location? 
•the semantics of his location? 
12
NextSteps& Future Work 
•Model & implementinferenceconsideringlocation semanticsand user sensitivities 
•Inferring user activity from a collection of location and semantic tag series 
•Private attributes such as age, gender, occupation 
•Reasoningabout causalityin the semanticlevel 
•Goingto a cinemaafterhavingdinnerat a nearbyrestaurant 
13
Future Work 
14 
Health-care 
(x, y)coordinates 
Geographical 
Semantics 
Visit 
Interactions/ 
Relationships 
Work 
Treatment 
Has sick friend 
Attributes 
Is Doctor 
Is Nurse 
Has 
Broken Leg 
Has Cancer 
Work Place 
Business 
Has customer 
User 
Adversary

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Semantic and Sensitivity Aware Location-Privacy Protection for the Internet of Things

  • 1. Semanticand SensitivityAwareLocation-PrivacyProtection for the Internet of Things Berker Ağır, Jean-Paul Calbimonte, Karl Aberer Workshop on Society, Privacy and the Semantic Web -Policy and Technology 2014 20 October2014
  • 2. Introduction •Online Devices •more infiltratingin dailylife •online services & applications •They are capable of sensing their environment and context GPS Accelerometer Barometer Thermometer 2
  • 3. PrivacyUnder Threat •Honest but curious server •Exploits all available data •With limited computational power, tries to infer private information Background knowledge on user history User Events Process according to objectives Perform attack Observed events Privacy Protection Mechanism(s) Application Server 3
  • 4. Location Privacy •Location data carries highly contextual information •Activity tracking •Inferring habits •Physical assault •Rich sensor environment and continuous connectivity •A non-stop and unbalanced threat on privacy 4
  • 5. Common Location-Privacy Protection Approaches ? Obfuscation Perturbation Hiding Anonymization Actual location Observed locations 5
  • 6. Shortcomings of Existing Approaches •Location information is multi- dimensional •Semantics •Not every location / semantic tagmight have the same importance in terms of privacy •Home location •Hospitals, restaurants •Overprotection •Service degradation 6
  • 7. Smart Adversariesand Strategies •Privacy has to be evaluated w.r.t. a real attack scenario •Adaptive protection mechanisms on user device •Move against each other in a strategic game •Location Semantics •User Mobility History •Common-knowledge sensitivities →Inference •Location Semantics •Adversary Modelling •Sensitivity Profile →Real-Time Adaptive Protection User Adversary 7
  • 8. Adaptive Location Privacy Protection 8 Adaptive Privacy Protection Mechanisms Privacy Estimation Module Estimate Candidate obfuscation area Sensitivity Profile Geographical& Semantic User History •Adaptive approach:Past behavior is considered before making a privacy decision •Causality and physical feasibility between transitions
  • 9. Sensitivity Profile Configuration Android application allowing to set semantic and geography based sensitivity levels 9
  • 10. Adaptive Protection in Action 10 Lowsensitivity-university High sensitivity-hospital
  • 11. SemanticLocation Privacy •Whatabout the privacyof the semantics? •Location mightnot matteras long as the user activityisunknown 11 Cinema? Pharmacy? Hotel? Hospital? Bar?
  • 12. EvaluatingPrivacy •What is the adversary’s errorin inferring •users’ geographical locations? •the semantics of user locations? •How confidentis the adversary? •Probabilistic nature of inference •What is the user’s desired privacy level (i.e., sensitivity) for •his geographical location? •the semantics of his location? 12
  • 13. NextSteps& Future Work •Model & implementinferenceconsideringlocation semanticsand user sensitivities •Inferring user activity from a collection of location and semantic tag series •Private attributes such as age, gender, occupation •Reasoningabout causalityin the semanticlevel •Goingto a cinemaafterhavingdinnerat a nearbyrestaurant 13
  • 14. Future Work 14 Health-care (x, y)coordinates Geographical Semantics Visit Interactions/ Relationships Work Treatment Has sick friend Attributes Is Doctor Is Nurse Has Broken Leg Has Cancer Work Place Business Has customer User Adversary