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Tim Finin University of Maryland, Baltimore County 29 January 2013

SRI Social Media Workshop Challenges for Large Scale Adoption of Social Media Context - Aware Privacy Policies in Mobile and Social Computing. Tim Finin University of Maryland, Baltimore County 29 January 2013 Joint work with Anupam Joshi, Laura Zavala and our students.

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Tim Finin University of Maryland, Baltimore County 29 January 2013

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  1. SRI Social Media WorkshopChallenges for Large Scale Adoption of Social MediaContext-Aware Privacy Policiesin Mobile and Social Computing Tim FininUniversity of Maryland, Baltimore County 29 January 2013 Joint work with Anupam Joshi, Laura Zavala and our students http://ebiq.org/r/349

  2. Convergence of Mobile and Social Social media & mobile computing are intertwined • We use laptops, tablets and smartphones more than desktops • Devices sync critical data with the cloud and each other • Take a picture on your smartphone and it gets uploaded to Instagram • Friend a person on Facebook via your computer, your smartphone notices and links her to existing contacts We should attend to both in addressing privacy by giving users ways to limit who can see what

  3. Context-Aware Privacy Social media apps and smart mobile devices each know a great deal about their users Together they may know too much! But acquiring and reasoning about this knowledge will enable both to provide better services

  4. Context-Aware Privacy We’re in a two-hour bud-get meeting in room 810 with Alice, Bob & Carol We’re in a impor-tant meeting We’re busy Sharing the information with other users, organizations and services can also be beneficial Context-aware policies can beused to limit information sharingand to control the actions and information access For both social media and mobile apps Two themes: situation awareness & information integration

  5. 1 Situational Awareness Awareness of what’s happeningaroundyou to understand howinformation, events and actionswill impact your goals and objectives A common theme in as we becomemoreinstrumented and interconnected Applies to people, smart interfaces, sensors, AI, wireless networks, embedded systems, streaming data, image processing, smartphones, etc. Challenges for distributed, dynamic & interconnected systems

  6. Information integration 2 You can’t use and integrate shared information unless you understand its meaning Common, shared semantic models (ontologies) are essential along with techniques for inference, knowledge mapping and provenance We use Semantic Web languages (RDF, OWL) as a standardized substrate to represent and reason with concepts, knowledge, facts, and rules. Since RDF is a graph-based representation, it’s a good fit for semantics-aware big data analytics

  7. E.g.: A Mobile Context KB • RDF KB on device conforming to shared ontologies • Imports ontologies, e.g. Foaf, Geo-Names • Uses Geonames linked data for background spatial knowledge • RDF supported by open source tools, standards, infra-structure, data <gn:Featurerdf:about="http://sws.geonames.org/4372143/"> <gn:name>UMBC</gn:name> <wgs84_pos:lat>39.25543</wgs84_pos:lat> <wgs84_pos:long>-76.71168</wgs84_pos:long> <wgs84_pos:alt>61</wgs84_pos:alt> <gn:parentFeaturerdf:resource="http://sws.geonames.org/4347790/"/> BaltimoreCounty <gn:parentCountryrdf:resource="http://sws.geonames.org/6252001/"/> United States <gn:parentADM1 rdf:resource="http://sws.geonames.org/4361885/"/>Maryland <gn:parentADM2 rdf:resource="http://sws.geonames.org/4347790/"/> Baltimore County </gn:Feature>

  8. Context / situation recognition Feature Vector Time, Noise level in db (avg, min, max), accel 3 axis (avg, min, max, magnitude, wifis, … Decision Trees Naïve Bayes SVM LinkedOpenData Train Classifiers RDF context model HMM

  9. Context-aware Privacy Policies We use declarative policies that can access the user’s profile and context model for privacy and security Privacy: one use is to control what user-sensitive information we share with whom and in what context Privacy and security: we use the same policy infrastructure to control actions that an app can take (e.g., turn on camera, access SD card)

  10. Ex: Sensor Data Access Policies • Lets users decide how their sensor information is released • Sample Privacy policy • share GPS co-ordinates on weekdays from 9am-5pm only if in office • Do not allow access to recorded audio but allow access to accelerometer and WiFi AP ids on weekdays

  11. Demonstration policies Share actual or mock location depending on requester [ ShareMockGPSSimple: (?user ex:systemUser ?someValue) (?requester ex:shareMockGPSCoordinates ``True') ] Policy to share mock location if user isinside Building10 [ShareMockGPSComplex1: (?user ex:systemUser ?someValue) (?someActivityplatys:occurs_at ?userPlace) (?userPlaceplatys:has_location ?userLocation) (?userLocationplatys:part_of ?userBuilding) (?userBuilding rdf:typeplatys:Building) equal(?userBuilding, platys:Building10) (?requester ex:shareMockGPSCoordinates ``True') ]

  12. Implemented use case Actual location reported to another Android app Obfuscated location provided to one Android app

  13. Conclusion Users of social media apps and mobile devices need better privacy controls Declarative policies grounded in semantic data offer expressive power We can mine mobile sensor data to learn models of activities and contexts We can mine social network content and structure to induce groups and sharing preferences

  14. finin@umbc.edu http://ebiquity.umbc.edu/

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