Semantics for Privacy and Context

1. Semantics for Privacy and Context Tim Finin University of Maryland, Baltimore County Joint work with Anupam Joshi, Prajit Das, Primal Pappachan,Eduado Mena and Roberto Yus http://ebiq.org/r/363

2. The plot outline • Today’s focus on big data requires semantics → Variety → Need for integration & fusion → Must understand data semantics → Use semantic languages & tools (reasoners, ML) → Have shared ontologies & background knowledge • Relevance to privacy and security • Protect personal information, esp. in mobile/IOT • Understanding and using context is often useful if not critical • Security relevant as as intrusions lead to loss of privacy

3. Use Case Examples We’ve used semantic technologies in support of assured information tasks including • Representing & enforcing information sharing policies • Negotiating for cloud services respecting organizational constraints (e.g., data privacy, location, …) • Modeling context for mobile users and using this to manage information sharing • Acquiring, using and sharing knowledge for situationally-aware intrusion detection systems Key technologies include Semantic Web languages (OWL, RDF) and tools and information extraction from text

4. Context-Aware Privacy & Security We’re in a two-hour budget meeting at X with A, B and C We’re in a impor-tant meeting We’re busy http://ebiq.org/p/589 Smart mobile devices know a great deal abouttheir users, including their current context Sensor data, email, calendar, social media, … Acquiring & using this knowledge helpsthem provide better services Context-aware policies can be used to limit information sharing as well as to control theactions and information access of mobile apps Sharing context with other users, organizationsand service providers can also be beneficial Context is more than time and GPS coordinates

5. Simple Context Ontology Light-weight, upper level context OWL ontology Centered around the concepts for: users, conceptual places, geo-places, activities, roles, space, and time Conceptual places such as at work and at home Activities occur at places &involve users filling roles LOD resources provide background knowledge

6. 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 Train Classifiers Train HMM models

7. Context-aware Privacy Policies We use declarative policies that can access the user’s profile and context model for privacy and security One use is to control what information we share with whom and in what context Another is to control the actions that an app can take (e.g., enable camera, access SD card) depending on the context A third is to obfuscate some shared information (e.g., location)

8. Context-aware Policies for Sharing Android's policies are limited • Privacy controls in existing  applications are limited • Friends Only and Invisible restrictions common • Not context-dependent but static and pre-determined • Controls to share other data largely non-existent

9. Context-aware Policies for Sharing Static Information Aspects of Context Generalization of Context Temporal Restrictions Requester’s Context Context Restrictions Android's policies are very limited • Privacy controls in existing location sharing applications are limited • Friends Only and Invisible restrictions common • Not context-dependent but static and pre-determined • Controls to share other data largely non-existent

10. Location Generalization GeoNames spatial containment knowledge from the LOD cloud is used when populating the KB • Share my location with manager on weekdays from 9am-5pm • User’s exact location in terms of GPS co-ordinates is shared The user may prohibit sharing GPS co-ordinates but permit sharing city-level location • Share my building-wide location with co workers not in my team on weekdays from 9am-5pm • Do not share location on weekends.

11. Location Generalization GeoNames spatial containment knowledge from the LOD cloud is used when populating the KB • Share my location with teachers on weekdays from 9am-5pm • User’s exact location in terms of GPS co-ordinates is shared • The user may prohibit sharing GPS co-ordinates but permit sharing city-level location • Share my building-wide location with teachers on weekdays from 9am-5pm

12. Activity Generalization • Share my activity with friends on weekends • User’s current activity shared with friends on weekends • Share more generalized activity rather that precise • confidential project meeting => Office Meeting => Working => Busy, Date => Meeting Friends • User clearly needs to obfuscate certain pieces of activity information to protect her context info • Share my public activity with friends on weekends • Public is a visibility option

13. Activity Generalization • Share my activity with friends on weekends • User’s current activity shared with friends on weekends • Share more generalized activity rather that precise • confidential project meeting => Working, Date => Meeting • User clearly needs to obfuscate certain pieces of activity information to protect her context info • Share my public activity with friends on weekends • Public is a visibility option

14. Context-aware power management Maintaining context model uses power We empirically determine power usage for a phone’s sensors and use this for optimization

15. Context-aware power management • When updating context model • Only enable sensors required by policy, reuse recent sensor readings whenever appropriate • e.g., disable GPS sensor when at home in evening • Prefer sensors with lower energy footprint or already in use when several available • e.g., Choose Wifi to GPS for location at office during day • Reorder rule conditions to reduce energy use • e.g., Check conditions requiring no sensor access first Maintaining the context model use power We developed an accurate power models for a phone’s sensors and use this for optimization http://ebiq.org/p/632

16. Collaborative Context Sharing • Like Blanche DuBois, we have always depended on the kindness of strangers • We are cooperative & ask one another for info. • Stanger on the street: Does this bus go to the aquarium? • Random classmate in next seat: When is HW6 due? • Devices can use ad hoc networks (e.g., Bluetooth) to query nearby devices for desired information • Each device uses a policy for what triples it’s willing to share with whom in what context •  Mobile Ad Hoc Knowledge Network

17. Collaboratively Constructed Contexts • A co-located group of devices can collaborate to share some context information • Exploit their different sensors and context detection/modeling capabilities • Consensus modeling can improve accuracy and overcome errors & malicious misinformation • Policies and context determine what to share with whom and in what context • We’ve designed an approach to detect/create groups and share information and used an Android prototype for simple evaluations

18. Collaborative Context Use Case Four GCC students with five devices in GCC library. All what to know where they are and what they’re doing

19. Collaborative Context Use Case Abed, Annie & Jeff are in a study group. Jeff has a phone and tablet. Pierce just happens to be there.

20. Collaborative Context Use Case Jeff’s phone knows it in room 7and that he’s talking; Annie’s tablet think’s she’s at home.

21. Context Sharing With help from context synthesizers, participants can have an appropriate consensus model Study group (Abed, Annie, Jeff): “study group about Spanish, duration of one hour, partici-pants: Jeff, Abed, Annie” In room (all): “in study room 7, in Greendale Community College, temp: 25oC, lights on” Jeff's devices: + "heart_rate:70bpm"

22. Context Ontology Assume devices use a shared, ontology for context Prototype uses JFact for DL reasoning on Android devices

23. Architecture Context providers have information to share Context synthesizers integrate, de-conflict & enrich data Prototype uses secure communication over Bluetooth

24. Context Groups Context synthesizer recognizes groups and creates default groups Predefined (e.g., ACM student chapter) Default groups created for identity, location and activity Provider’s own policies control what is shared with a group

25. Context integration and reconciliation coments

26. Faceblock Click image to play 80 second video or go to Youtube http://ebiq.org/p/666

27. Conclusion http://ebiq.org/r/363 • Google’s new slogan: things, not strings • We can construct context models in semantic languages using data from sensors, calendars and other sources • Semantic policies for information sharing can manage what is shared with whom and in what context • Additional protocols and infrastructure will permit dynamic collaborative context models