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A Semantic Context-aware Privacy Model for FaceBlock

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Wearable computing devices like Google Glass are at the forefront of technological evolution in smart devices. The ubiquitous and oblivious nature of photography using these devices has made people concerned about their privacy in private and public settings. The Face- Block project protects the privacy of people around Glass users by making pictures taken by the latter, Privacy-Aware. Through sharing of privacy policies, users can choose whether or not to be included in pictures. However, the current privacy model of FaceBlock only permits simple constraints such as allow versus disallow pictures. In this paper, we present an extended context-aware privacy model represented using OWL ontologies and SWRL rules. We also describe use cases of how this model can help FaceBlock to generate Privacy-Aware Pictures depending on context and privacy needs of the user.

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A Semantic Context-aware Privacy Model for FaceBlock

  1. 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
  2. 2. Cameras are Ubiquitous
  3. 3. Social Networks
  4. 4. “Invisible” Cameras
  5. 5. Technology v/s Privacy News Paper articles http://www.youtube.com/watch?v=ClvI9fZaz6M
  6. 6. Solutions… Really?
  7. 7. Introducing FaceBlock http://www.youtube.com/watch?v=IseoIWNWiR8
  8. 8. Privacy-Aware Pictures
  9. 9. How it works?
  10. 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. 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. 12. Context-Aware “[...] any information that can be used to characterize the situation of an entity” Dey and Abowd
  13. 13. Privacy Policies For expressing user preference on pictures Constraints based on user context model Semantic Web Rule Language (SWRL)
  14. 14. Context Pieces Location based Activity based Unique ID Time
  15. 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. 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. 17. Others Wishes to protect his privacy at the party Generates face identifier Specifies context constraints using rules Runs FaceBlock in the background
  18. 18. Protocol Exchange Identity Share Face Identifier I: L: At T: Beach Colleague House Context Recognition A: Party Weekend Policy Triggered PrimalID, FaceBlock: True
  19. 19. Other scenarios
  20. 20. Challenges Image Face Recognition / Detection / Identifier Generation Communication Malicious Policies
  21. 21. Challenges Context and Policy Imprecise context Policies - Generation, Conflict Resolution, Validity General Privacy Loss Enforcement or Incentivizing Energy Cost
  22. 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

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