A Semantic Context-aware Privacy Model for FaceBlock
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The document proposes a semantic context-aware privacy model called FaceBlock that uses semantic web technologies to dynamically infer user preferences about having their photo taken based on contextual information like location, time, activity, and relationships. It describes how FaceBlock works by exchanging identity and face identifier information between users' devices, recognizing the context, and triggering privacy policies to determine if a photo should be allowed or obscured. Some challenges discussed include face recognition, context and policy management, and ensuring user privacy is maintained.
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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
- 19. Other scenarios
- 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
Notes de l'éditeur
- Lot of cameras
- and the result - me being tweet/retweeted on twitter (social networks) without my knowledge
- With new technology such as google glass, people are becoming more paranoid about technology
- In this famous tv show, the presenter is taking on Google Glass as a privacy nightmare
- Non-Technological Solution Technical Solution - but not a practical approach, people can still take pictures
- 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
- 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
- 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
- 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.
- An user preference on whether his face should be included in the picture or not (Safe) SWRL rule for expressing policies
- 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)
- 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).
- FaceBlock Google Glass user
- FaceBlock Smartphone user
- (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
- 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
- False positives, People not looking directly into the camera, bad quality pictures Masquerading as someone else
- Energy Cost - as we are running the reasoner, face recognition, context extraction on mobile device, energy is an issue.
- Smarter - Fine grained representation of user context, Inferring implicit knowledge based on explicit facts
- Put face block website url/ twitter/ g+ Put affiliations in the last slide
- Affiliations Contact information
- 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).
- 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.