Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.
Semanticand SensitivityAwareLocation-PrivacyProtection for the Internet of Things 
Berker Ağır, Jean-Paul Calbimonte, Karl...
Introduction 
•Online Devices 
•more infiltratingin dailylife 
•online services & applications 
•They are capable of sensi...
PrivacyUnder Threat 
•Honest but curious server 
•Exploits all available data 
•With limited computational power, tries to...
Location Privacy 
•Location data carries highly contextual information 
•Activity tracking 
•Inferring habits 
•Physical a...
Common Location-Privacy Protection Approaches 
? 
Obfuscation 
Perturbation 
Hiding 
Anonymization 
Actual location 
Obser...
Shortcomings of Existing Approaches 
•Location information is multi- dimensional 
•Semantics 
•Not every location / semant...
Smart Adversariesand Strategies 
•Privacy has to be evaluated w.r.t. a real attack scenario 
•Adaptive protection mechanis...
Adaptive Location Privacy Protection 
8 
Adaptive Privacy Protection Mechanisms 
Privacy Estimation Module 
Estimate 
Cand...
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 activityis...
EvaluatingPrivacy 
•What is the adversary’s errorin inferring 
•users’ geographical locations? 
•the semantics of user loc...
NextSteps& Future Work 
•Model & implementinferenceconsideringlocation semanticsand user sensitivities 
•Inferring user ac...
Future Work 
14 
Health-care 
(x, y)coordinates 
Geographical 
Semantics 
Visit 
Interactions/ 
Relationships 
Work 
Treat...
Prochain SlideShare
Chargement dans…5
×

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

1 040 vues

Publié le

Everyday applications and ubiquitous devices contribute data to the Internet of Things, oftentimes including sensitive information of people. This opens new challenges for protecting users' data from adversaries, who can perform different types of attacks using combinations
of private and publicly available information. In this work, we discuss some of the main challenges, especially regarding location-privacy, and a general approach for adaptively protecting this type of data. This approach considers the semantics of the user location, as well as the user's sensitivity preferences, and also builds an adversary model for estimating privacy levels.

Publié dans : Technologie
  • Soyez le premier à commenter

  • Soyez le premier à aimer ceci

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

  1. 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. 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. 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. 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. 5. Common Location-Privacy Protection Approaches ? Obfuscation Perturbation Hiding Anonymization Actual location Observed locations 5
  6. 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. 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. 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. 9. Sensitivity Profile Configuration Android application allowing to set semantic and geography based sensitivity levels 9
  10. 10. Adaptive Protection in Action 10 Lowsensitivity-university High sensitivity-hospital
  11. 11. SemanticLocation Privacy •Whatabout the privacyof the semantics? •Location mightnot matteras long as the user activityisunknown 11 Cinema? Pharmacy? Hotel? Hospital? Bar?
  12. 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. 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. 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

×