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Semantic Knowledge and Privacy in the Physical Web

Semantic Knowledge and Privacy in the Physical Web

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In the past few years, the Internet of Things has started to become a reality; however, its growth has been hampered by privacy and security concerns. One promising approach is to use Semantic Web technologies to mitigate privacy concerns in an informed, flexible way. We present CARLTON, a framework for managing data privacy for entities in a Physical Web deployment using Semantic Web technologies. CARLTON uses context-sensitive privacy policies to protect privacy of organizational and personnel data. We provide use case scenarios where natural language queries for data are handled by the system, and show how privacy policies may be used to manage data privacy in such scenarios, based on an ontology of concepts that can be used as rule antecedents in customizable privacy policies.

In the past few years, the Internet of Things has started to become a reality; however, its growth has been hampered by privacy and security concerns. One promising approach is to use Semantic Web technologies to mitigate privacy concerns in an informed, flexible way. We present CARLTON, a framework for managing data privacy for entities in a Physical Web deployment using Semantic Web technologies. CARLTON uses context-sensitive privacy policies to protect privacy of organizational and personnel data. We provide use case scenarios where natural language queries for data are handled by the system, and show how privacy policies may be used to manage data privacy in such scenarios, based on an ontology of concepts that can be used as rule antecedents in customizable privacy policies.

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Semantic Knowledge and Privacy in the Physical Web

  1. 1. Semantic Knowledge and Privacy in the Physical Web PRAJIT KUMAR DAS, ABHAY KASHYAP, GURPREET SINGH, CYNTHIA MATUSZEK, TIM FININ, ANUPAM JOSHI UMBC ebiquity and IRAL Labs
  2. 2. Motivation Our goal is to provide contextually aware information, using the IoT, that is privacy preserving and ubiquitously helpful Image courtesy Batman Wikia CARLTON Slide 2 of 44
  3. 3. IoT by Volume Slide 3 of 44
  4. 4. IoT by Domain Slide 4 of 44
  5. 5. IoT by Privacy Concerns Slide 5 of 44
  6. 6. Salient features  CARLTON: A context-aware, NL question-answer BOT  Context derived from the Physical Web (IoT)  Under development, prototype system  Simple NLP using tools like Stanford CoreNLP  Mobile app and Kiosk for front-end  ABAC privacy model, Privacy rules using SWRL  Hierarchical context ontology  Optional authentication for UMBC people Slide 6 of 44
  7. 7.  Concretization of IoT  Small, quick seamless interactions with physical objects and locations with your device Physical web: What? Slide 7 of 44
  8. 8. Physical web: What?  Everything is a tap away Slide 8 of 44
  9. 9. Physical web: What?  See what’s useful around you Slide 9 of 44
  10. 10. Physical web: What?  Any object or place can broadcast content Slide 10 of 44
  11. 11. Physical web: How? Three main techniques  Nearby Connections Slide 11 of 44
  12. 12. Physical web: How? Three main techniques  Nearby Connections  Nearby Notifications Slide 12 of 44
  13. 13. Physical web: How? Three main techniques  Nearby Connections  Nearby Notifications  Nearby Messages Slide 13 of 44
  14. 14. System Overview Slide 14 of 44
  15. 15. System Overview Slide 15 of 44
  16. 16. System Overview Slide 16 of 44
  17. 17. Who is Tim Finin? System Overview Slide 17 of 44
  18. 18. “Tim Finin”: Person Entity type “Who”: WH query type Text to Semi-Structured Text Intent Who is Tim Finin? System Overview Slide 18 of 44
  19. 19. “Tim Finin”: Person Entity type “Who”: WH query type Text to Semi-Structured Text Intent SPARQL query generator Context Who is Tim Finin? System Overview Slide 19 of 44
  20. 20. “Tim Finin”: Person Entity type “Who”: WH query type Text to Semi-Structured Text Intent SPARQL query generator Context Who is Tim Finin? Inference Engine OntologyKnowledge base System Overview Slide 20 of 44
  21. 21. “Tim Finin”: Person Entity type “Who”: WH query type Text to Semi-Structured Text Intent SPARQL query generator Context Who is Tim Finin? Inference EngineResponse: JSON {“text”: “He’s a Professor in the Computer Science department!”} OntologyKnowledge base System Overview Slide 21 of 44
  22. 22. Example query Slide 22 of 44
  23. 23. Is this room booked from 2PM-3PM? Example query Slide 23 of 44
  24. 24. User is a faculty and is in front of Conf. room 1. Is this room booked from 2PM-3PM? Example query Slide 24 of 44
  25. 25. Conf. room 1 calendar has no events during that time. Is this room booked from 2PM-3PM? Example query Slide 25 of 44
  26. 26. Is this room booked from 2PM-3PM? No, would you like me to book it from 2PM – 3PM? Example query Slide 26 of 44
  27. 27. Is this room booked from 2PM-3PM? No, would you like me to book it from 2PM – 3PM? Yes, please! Example query Slide 27 of 44
  28. 28. Okay, the room has been booked in your name from 2PM – 3PM Is this room booked from 2PM-3PM? No, would you like me to book it from 2PM – 3PM? Yes, please! Example query Slide 28 of 44
  29. 29. Example query Slide 29 of 44
  30. 30. Is Dr. Joshi here? Example query Slide 30 of 44
  31. 31. Is Dr. Joshi here? User could mean Dr. A. Joshi or Dr. K. Joshi. Example query Slide 31 of 44
  32. 32. Is Dr. Joshi here? But user is in front of Dr. A. Joshi’s office. Example query Slide 32 of 44
  33. 33. Is Dr. Joshi here? User is an advisee of Dr. A. Joshi Example query Slide 33 of 44
  34. 34. Is Dr. Joshi here? Dr. Joshi is in a meeting till 3PM Example query Slide 34 of 44
  35. 35. Example query Slide 35 of 44
  36. 36. Example query Where is Dr. Finin’s office? Slide 36 of 44
  37. 37. Example query User is in CSEE building Where is Dr. Finin’s office? Slide 37 of 44
  38. 38. Example query User is unknown to system Where is Dr. Finin’s office? Slide 38 of 44
  39. 39. Example query Where is Dr. Finin’s office? Please see CSEE front desk for required information Slide 39 of 44
  40. 40. Example1. @prefix crltn:<https://www.ebiquity.org/ontologies/carlton/0.1>. @prefix swrlb:<http://www.w3.org/2003/11/swrlb>. crltn: student(?requester)∧ ( crltn: supervises(“Xavier”,?requester)∨ (crltn: affiliatedWith(?requester,?labName)∧crltn: leads(“Xavier”,?labName)) )∧ crltn: hasCurrentLocation(?requester,?aBldgLocation)∧ crltn: room(?aBldgLocation)∧crltn: sitsIn(“Xavier”,?aBldgLocation)∧ crltn: currentTime(?currTime)∧swrlb: Exists(?anEvent)∧crltn: speakingAt(“Xavier”,?anEvent)∧ ( (crltn: startTime(?anEvent,?eventStartTime)∧swrlb: greaterThan(?eventStartTime,?currTime))∨ (crltn: endTime(?anEvent,?eventEndTime)∧swrlb: greaterThan(?currTime,?eventEndTime)) )∧crltn: hasCurrentLocation(“Xavier”,?aLocation)∧crltn: Location(?aLocation)∧ crltn: requestLocation(“Xavier”) ⇒ shareLocation(?aLocation) Policy Example Slide 40 of 44
  41. 41. supervises(“Xavier”,?requester) OR ( affiliatedWith(?requester,?labName) AND leads(“Xavier”,?labName) ) Policy Example Slide 41 of 44
  42. 42. hasCurrentLocation(?requester,?aBldgLocation) AND room(?aBldgLocation) AND sitsIn(“Xavier”,?aBldgLocation) => shareLocation(?aLocation) Policy Example Slide 42 of 44
  43. 43. Future work  Prototype system constantly adding conversations  Beacons on robots  Reason over robots near you  How robots respond to instructions?  “Can you take me to Prof. Matuszek now?”  “Show me the way to the ITE 346 conference room” Slide 43 of 44
  44. 44. Summary  We presented CARLTON  A context-aware, NL question-answer BOT  Context derived from the Physical Web (IoT)  Semantic web technologies used to preserve data privacy Thanks to NSF for the travel grant! and Thanks to Google for the gift of beacons! Slide 44 of 44

Editor's Notes

  • We present Carlton, a context-aware, natural language, question-answer BOT that responds to ‘Helpdesk’ styled questions using Semantic Web technologies
    It leverages context derived from the Physical Web to protect privacy of organization’s and organizational entity’s data, by executing context-sensitive SWRL rules

    Carlton is a conversational Bot that uses the Internet of Things to respond to queries tying in information about the environment
  • Roughly half of all consumers highly uncomfrotable with companies using and selling their data in physical spaces
    Altimeter is part of Prophet, a consultancy company that helps clients find better ways to grow. Altimeter, is a research and consulting firm that helps companies understand – and act on – digital disruption.
    Ratings of users in a range of 1-5, 1 being extremely uncomfortable and 2 being uncomfortable about the situation

    Source: Consumer perceptions of the Internet of Things based on 2062 respondents
  • Carlton has a memory and remembers the question asked before the current one
  • Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device.

    Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away.
    PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30
    A city rent-a-bike service could enable users to sign up on the spot

    See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you.
    SpotCall HumaneSociety
    A dog collar could allow passerby to call a service to find the owner

    Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless.
    A bus that could alert users of its next stop
    A home appliance could offer an interactive tutorial.
    An industrial robot could display diagnostic information.
    A mall that could offer a map.


  • Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device.

    Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away.
    PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30
    A city rent-a-bike service could enable users to sign up on the spot

    See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you.
    SpotCall HumaneSociety
    A dog collar could allow passerby to call a service to find the owner

    Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless.
    A bus that could alert users of its next stop
    A home appliance could offer an interactive tutorial.
    An industrial robot could display diagnostic information.
    A mall that could offer a map.


  • Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device.

    Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away.
    PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30
    A city rent-a-bike service could enable users to sign up on the spot

    See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you.
    SpotCall HumaneSociety
    A dog collar could allow passerby to call a service to find the owner

    Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless.
    A bus that could alert users of its next stop
    A home appliance could offer an interactive tutorial.
    An industrial robot could display diagnostic information.
    A mall that could offer a map.


  • Combining radio signals and an ultrasonic modem that uses the speakers and microphone of a mobile to determine that you are in close proximity to another device.

    Everything is a tap away: Walk up and interact with any object -- a parking meter, a toy, a poster -- or location -- a bus stop, a museum, a store -- without installing an app first. Interactions are only a tap away.
    PAIDPAY00:0000:1000:2000:3000:0000:1000:2000:30
    A city rent-a-bike service could enable users to sign up on the spot

    See what’s useful around you: See web pages associated with the space around you. Choose the page most useful to you.
    SpotCall HumaneSociety
    A dog collar could allow passerby to call a service to find the owner

    Any object or place can broadcast content: When anything can offer information and utility, the possibilities are endless.
    A bus that could alert users of its next stop
    A home appliance could offer an interactive tutorial.
    An industrial robot could display diagnostic information.
    A mall that could offer a map.


  • Uses BLE and ultrasonic modem to get a URL to a phone!
    Everything else is standard web technology or standard computing technology.
    Nearby Messages: Provides Publish Subscribe methods relying on proximity
    Nearby Connections: Enables local network real-time connect and exchange
    Nearby Notifications: Android feature associating website or app to beacon
  • Uses BLE and ultrasonic modem to get a URL to a phone!
    Everything else is standard web technology or standard computing technology.
    Nearby Messages: Provides Publish Subscribe methods relying on proximity
    Nearby Connections: Enables local network real-time connect and exchange
    Nearby Notifications: Android feature associating website or app to beacon
  • Uses BLE and ultrasonic modem to get a URL to a phone!
    Everything else is standard web technology or standard computing technology.
    Nearby Messages: Provides Publish Subscribe methods relying on proximity
    Nearby Connections: Enables local network real-time connect and exchange
    Nearby Notifications: Android feature associating website or app to beacon
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • Nearby Messages API provides data from beacons to kiosks and Android phones
    Stanford CoreNLP suite of tools used to do POS tagging, parsing and identify entities and relations
    Context discovery of user and requester
    antecedent ⇒ consequent
    Antecedents: Requester context, Entity metadata, Entity context
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Faculty Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar No, do you want me to book it from 2PM – 3PM?
    Student Is this room booked from 2PM-3PM? Conference room ITE Conference room 346 User id; Room calendar Please see CSEE office for booking

    AppointmentRequest class addition
    ValidReservationRequest as subclass of Restriction has_requestor => Faculty
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Advisee Is Dr. Joshi here? Dr. A. Joshi Department office User identity; Target disambiguation; Target location Dr. Joshi is in a meeting till 3PM
    Student in class Is Dr. Joshi here? Dr. K. Joshi In front of faculty office User identity; Target disambiguation; Target location No, but I can tell you her office hours
  • User Query Target Requester Location Additional context Response
    Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information
    Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  • User Query Target Requester Location Additional context Response
    Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information
    Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  • User Query Target Requester Location Additional context Response
    Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information
    Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  • User Query Target Requester Location Additional context Response
    Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information
    Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  • User Query Target Requester Location Additional context Response
    Visitor Where is Dr. Finin’s office? Dr. T. Finin ITE Building Target disambiguation; Please see ITE front desk for information
    Student Where is Dr. Finin’s office? Dr. T. Finin ITE Building User Id; Target disambiguation; His office is in ITE 332
  • Complete privacy reasoning isomorphic to Truth maintenance
    Truth maintenance - if information is being added in a forward manner how do you ensure that your knowledge already stored are still true -

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