SlideShare ist ein Scribd-Unternehmen logo
1 von 22
A Semantic Context-Aware 
Privacy Model for 
FaceBlock 
Primal Pappachan, Roberto Yus, Prajit Kumar 
Das, Tim Finin, Eduardo Mena, and Anupam 
Joshi 
http://face-block.me
Cameras are Ubiquitous
Social Networks
“Invisible” Cameras
Technology v/s Privacy 
News Paper articles 
http://www.youtube.com/watch?v=ClvI9fZaz6M
Solutions
 Really?
Introducing FaceBlock 
http://www.youtube.com/watch?v=IseoIWNWiR8
Privacy-Aware Pictures
How it works?
All in or nothing? 
A person’s preferences would depend on her 
context (e.g., time, place, or activity) 
Examples 
“I am okay being photographed by people 
I know at a private event” 
“I do not like to be photographed when I 
am at public places”.
Semantic Web 
Technologies 
Understand the semantics of concepts 
such as “public place”, “people I know” 
or “private event” 
Semantically represent privacy policies 
based on concepts 
Dynamically infer user preferences 
about pictures based on context
Context-Aware 
“[...] any information that can be used to 
characterize the situation of an entity” Dey 
and Abowd
Privacy Policies 
For expressing user 
preference on pictures 
Constraints based on user 
context model 
Semantic Web Rule 
Language (SWRL)
Context Pieces 
Location based 
Activity based 
Unique ID 
Time
Example Policy 
“do not allow my social network colleagues 
group (identity context) to take pictures of 
me (identity context) at parties (activity 
context) held on weekends (time context) at the beach 
house (location context)”
Glass User 
Wishes to take pictures at the party 
Runs FaceBlock in the background 
Receives face identifiers and policies 
Detects, recognizes and obscures the 
faces as necessary
Others 
Wishes to protect his privacy at the party 
Generates face identifier 
Specifies context constraints using rules 
Runs FaceBlock in the background
Protocol 
Exchange Identity 
Share Face Identifier 
I: 
L: At T: 
Beach 
Colleague 
House 
Context Recognition 
A: Party 
Weekend 
Policy Triggered 
PrimalID, FaceBlock: True
Other scenarios
Challenges 
Image 
Face Recognition / Detection / 
Identifier Generation 
Communication 
Malicious Policies
Challenges 
Context and Policy 
Imprecise context 
Policies - Generation, Conflict Resolution, Validity 
General 
Privacy Loss 
Enforcement or Incentivizing 
Energy Cost
Take aways 
Users are defenseless against loss of privacy in 
pictures 
Novel approach for taking privacy-aware 
pictures 
Semantic Web technologies makes FaceBlock 
smarter 
Proof-of-concept implementation 
http://face-block.me Thank you NSF and SWSA

Weitere Àhnliche Inhalte

Andere mochten auch (6)

Mobipedia presentation
Mobipedia presentationMobipedia presentation
Mobipedia presentation
 
An ontology based sensor selection engine
An ontology based sensor selection engineAn ontology based sensor selection engine
An ontology based sensor selection engine
 
Pythonizing the Indian Engineering Education
Pythonizing the Indian Engineering EducationPythonizing the Indian Engineering Education
Pythonizing the Indian Engineering Education
 
Droidcon India 2011 Talk
Droidcon India 2011 TalkDroidcon India 2011 Talk
Droidcon India 2011 Talk
 
FOSSEE
FOSSEEFOSSEE
FOSSEE
 
Cenitpede: Analyzing Webcrawl
Cenitpede: Analyzing WebcrawlCenitpede: Analyzing Webcrawl
Cenitpede: Analyzing Webcrawl
 

Ähnlich wie A Semantic Context-aware Privacy Model for FaceBlock

CS260
CS260CS260
CS260
mor
 
PRIVACY PRESERVATION IN SOCIAL MEDIA BY IMAGE PROCESSING
PRIVACY PRESERVATION  IN  SOCIAL MEDIA BY IMAGE PROCESSINGPRIVACY PRESERVATION  IN  SOCIAL MEDIA BY IMAGE PROCESSING
PRIVACY PRESERVATION IN SOCIAL MEDIA BY IMAGE PROCESSING
AM Publications
 
techKNOW leadership for JefCoEd Tech Camp 7.11.13
techKNOW leadership for JefCoEd Tech Camp 7.11.13techKNOW leadership for JefCoEd Tech Camp 7.11.13
techKNOW leadership for JefCoEd Tech Camp 7.11.13
mwilson518
 
The Dark Side Of The Web
The Dark Side Of The WebThe Dark Side Of The Web
The Dark Side Of The Web
mshin
 
PARC CSL Colloquium
PARC CSL ColloquiumPARC CSL Colloquium
PARC CSL Colloquium
mor
 
Proactive Displays, UW DUB group, 16 July 2008
Proactive Displays, UW DUB group, 16 July 2008Proactive Displays, UW DUB group, 16 July 2008
Proactive Displays, UW DUB group, 16 July 2008
Joe McCarthy
 

Ähnlich wie A Semantic Context-aware Privacy Model for FaceBlock (20)

Privacy Considerations in Online and Mobile Photo Sharing
Privacy Considerations in Online and Mobile Photo SharingPrivacy Considerations in Online and Mobile Photo Sharing
Privacy Considerations in Online and Mobile Photo Sharing
 
MyTweetFace
MyTweetFaceMyTweetFace
MyTweetFace
 
MyTweetFace
MyTweetFaceMyTweetFace
MyTweetFace
 
Your Digital Stomping Ground
Your Digital Stomping GroundYour Digital Stomping Ground
Your Digital Stomping Ground
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
CS260
CS260CS260
CS260
 
PRIVACY PRESERVATION IN SOCIAL MEDIA BY IMAGE PROCESSING
PRIVACY PRESERVATION  IN  SOCIAL MEDIA BY IMAGE PROCESSINGPRIVACY PRESERVATION  IN  SOCIAL MEDIA BY IMAGE PROCESSING
PRIVACY PRESERVATION IN SOCIAL MEDIA BY IMAGE PROCESSING
 
techKNOW leadership for JefCoEd Tech Camp 7.11.13
techKNOW leadership for JefCoEd Tech Camp 7.11.13techKNOW leadership for JefCoEd Tech Camp 7.11.13
techKNOW leadership for JefCoEd Tech Camp 7.11.13
 
The Dark Side Of The Web
The Dark Side Of The WebThe Dark Side Of The Web
The Dark Side Of The Web
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
Proactive Displays: Bridging the Gaps between Online Social Networks and Shar...
Proactive Displays: Bridging the Gaps between Online Social Networks and Shar...Proactive Displays: Bridging the Gaps between Online Social Networks and Shar...
Proactive Displays: Bridging the Gaps between Online Social Networks and Shar...
 
Social network and digital security
Social network and digital securitySocial network and digital security
Social network and digital security
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
Lesson plan day 1
Lesson plan day 1Lesson plan day 1
Lesson plan day 1
 
MIT CSAIL HCI Seminar
MIT CSAIL HCI SeminarMIT CSAIL HCI Seminar
MIT CSAIL HCI Seminar
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
PARC CSL Colloquium
PARC CSL ColloquiumPARC CSL Colloquium
PARC CSL Colloquium
 
Factual research pro forma
Factual research pro formaFactual research pro forma
Factual research pro forma
 
Proactive Displays, UW DUB group, 16 July 2008
Proactive Displays, UW DUB group, 16 July 2008Proactive Displays, UW DUB group, 16 July 2008
Proactive Displays, UW DUB group, 16 July 2008
 

KĂŒrzlich hochgeladen

Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

KĂŒrzlich hochgeladen (20)

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Mcleodganj Call Girls đŸ„° 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls đŸ„° 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls đŸ„° 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls đŸ„° 8617370543 Service Offer VIP Hot Model
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 

A Semantic Context-aware Privacy Model for FaceBlock

  • 1. A Semantic Context-Aware Privacy Model for FaceBlock Primal Pappachan, Roberto Yus, Prajit Kumar Das, Tim Finin, Eduardo Mena, and Anupam Joshi http://face-block.me
  • 5. Technology v/s Privacy News Paper articles http://www.youtube.com/watch?v=ClvI9fZaz6M
  • 10. All in or nothing? A person’s preferences would depend on her context (e.g., time, place, or activity) Examples “I am okay being photographed by people I know at a private event” “I do not like to be photographed when I am at public places”.
  • 11. Semantic Web Technologies Understand the semantics of concepts such as “public place”, “people I know” or “private event” Semantically represent privacy policies based on concepts Dynamically infer user preferences about pictures based on context
  • 12. Context-Aware “[...] any information that can be used to characterize the situation of an entity” Dey and Abowd
  • 13. Privacy Policies For expressing user preference on pictures Constraints based on user context model Semantic Web Rule Language (SWRL)
  • 14. Context Pieces Location based Activity based Unique ID Time
  • 15. Example Policy “do not allow my social network colleagues group (identity context) to take pictures of me (identity context) at parties (activity context) held on weekends (time context) at the beach house (location context)”
  • 16. Glass User Wishes to take pictures at the party Runs FaceBlock in the background Receives face identifiers and policies Detects, recognizes and obscures the faces as necessary
  • 17. Others Wishes to protect his privacy at the party Generates face identifier Specifies context constraints using rules Runs FaceBlock in the background
  • 18. Protocol Exchange Identity Share Face Identifier I: L: At T: Beach Colleague House Context Recognition A: Party Weekend Policy Triggered PrimalID, FaceBlock: True
  • 20. Challenges Image Face Recognition / Detection / Identifier Generation Communication Malicious Policies
  • 21. Challenges Context and Policy Imprecise context Policies - Generation, Conflict Resolution, Validity General Privacy Loss Enforcement or Incentivizing Energy Cost
  • 22. Take aways Users are defenseless against loss of privacy in pictures Novel approach for taking privacy-aware pictures Semantic Web technologies makes FaceBlock smarter Proof-of-concept implementation http://face-block.me Thank you NSF and SWSA

Hinweis der Redaktion

  1. Lot of cameras
  2. and the result - me being tweet/retweeted on twitter (social networks) without my knowledge
  3. With new technology such as google glass, people are becoming more paranoid about technology
  4. In this famous tv show, the presenter is taking on Google Glass as a privacy nightmare
  5. Non-Technological Solution Technical Solution - but not a practical approach, people can still take pictures
  6. Takes a picture of the user and generates a mathematical representation of the face which is called the eigen face Sends this information and policy which is don't take pictures using P2P networks such as bluetooth or WiFi Uses face detection, face recognition to identify faces of user in picture and obscure them
  7. An all-or-nothing model does not help in many real-life situations User preferences- who is taking the picture and with whom it may be shared
  8. An all-or-nothing model does not help in many real-life situations Ontologies and reasoner can be useful for making the privacy model higher granularity and better control
  9. An entity is a person, place, or object that is considered relevant to the interaction between a user and application, including the user and applications themselves. We used a simple ontology for our implementation involving the use cases we were looking at and is based on the definition by Dey and Abowd.
  10. An user preference on whether his face should be included in the picture or not (Safe) SWRL rule for expressing policies
  11. Usage of ontologies enables FaceBlock to apply privacy policy for specialization of general concepts. For example, if a student has specified that she does not want her pictures to be taken at the University buildings, it is assumed that she does not want any pictures to be taken at the University library unless specified by policy that she does not mind pictures being taken at the library. Definition of context Synthesizer - Example policies which would be activity dependent are: “don’t allow my picture when I’m dancing” (shared by a user), “don’t allow my picture during meetings” (shared by a meeting room). The later policy will be applied to different types of meetings defined in the ontology (e.g., business meeting, research meeting). Identity - Used for identifying users on first contact Unique User ID (MAC ID, Social Network Ontologies)
  12. SWRL rules - to model whether a user is allowed to take picture or not of another one we use the data property FaceBlockPictures(Person,xsd:boolean).
  13. FaceBlock Google Glass user
  14. FaceBlock Smartphone user
  15. (P) Context Recognition (P) Triggering Context Constraint and Sending Policy (R) Receiving Policy and Taking picture (R) Detecting, Recognizing Face and blurring it in the picture
  16. FaceBlock is not only for users, its also for location and activities Location broadcasting policies - e.g.: church, museum etc. (tourists) Events broadcasting policies - for example a confidential presentation Photographers wishing to cut off from unnecessary interferences
  17. False positives, People not looking directly into the camera, bad quality pictures Masquerading as someone else
  18. Energy Cost - as we are running the reasoner, face recognition, context extraction on mobile device, energy is an issue.
  19. Smarter - Fine grained representation of user context, Inferring implicit knowledge based on explicit facts
  20. Put face block website url/ twitter/ g+ Put affiliations in the last slide
  21. Affiliations Contact information
  22. Definition of context Synthesizer - Example policies which would be activity dependent are: “don’t allow my picture when I’m dancing” (shared by a user), “don’t allow my picture during meetings” (shared by a meeting room). The later policy will be applied to different types of meetings defined in the ontology (e.g., business meeting, research meeting).
  23. Usage of ontologies enables FaceBlock to apply privacy policy for specialization of general concepts. For example, if a student has specified that she does not want her pictures to be taken at the University buildings, it is assumed that she does not want any pictures to be taken at the University library unless specified by policy that she does not mind pictures being taken at the library.