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
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
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
Context-Aware Privacy & Security
• Smart mobile devices know a great deal about
their users, including their current context
• Sensor data, email, calendar, social media, …
in a two-hour
• Acquiring & using this knowledge helps We’re
budget meeting at X
with A, B and C
them provide better services
We’re in a imporWe’re busy
• Context-aware policies can be used to limit
tant meeting
information sharing as well as to control the
actions and information access of mobile apps
• Sharing context with other users, organizations
and service providers can also be beneficial
• Context is more than time and GPS coordinates
http://ebiq.org/p/589
Simple Context Ontology
• Light-weight, upper level
context OWL ontology
• Centered around the
concepts for: users,
conceptual places, geoplaces, 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
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
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)
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 predetermined
• Controls to share other data largely
non-existent
Context-aware Policies for Sharing
Android's policies are very
limited
Static
Information
• Privacy controls in existing location
sharing applications are limited
Aspects of
Context
Generalization
of Context
– Friends
Only and Invisible restrictions common
Temporal
Restrictions
– Not context-dependent but static and preRequester’s
determined
Context
• Controls to share other data largely
non-existent
Context
Restrictions
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.
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
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
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
Context-aware power management
• Maintaining context model uses power
• We empirically determine power usage for a
phone’s sensors and use this for optimization
Context-aware power management
When
updating
context
model
• Maintaining
the context
model
use power
1. Only enable sensors required by policy, reuse
• We developed an accurate power models for a
recent sensor readings whenever appropriate
phone’s
sensors
and
useatthis
optimization
e.g., disable
GPS sensor
when
homefor
in evening
2. 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
3.Reorder rule conditions to reduce energy use
e.g., Check conditions requiring no sensor access first
http://ebiq.org/p/632
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
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
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
Collaborative Context Use Case
Abed, Annie & Jeff are in a study group. Jeff has a
phone and tablet. Pierce just happens to be there.
Collaborative Context Use Case
Jeff’s phone knows it in room 7 and that he’s talking;
Annie’s tablet think’s she’s at home.
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, participants: Jeff, Abed, Annie”
• In room (all): “in study room 7, in Greendale
o
Community College, temp: 25 C, lights on”
• Jeff's devices: + "heart_rate:70bpm"
Context Ontology
• Assume devices
use a shared,
ontology for
context
• Prototype uses
JFact for DL
reasoning on
Android devices
Architecture
MobileGDevice
• Context providers have
information to share
• Context synthesizers
integrate, de-conflict &
enrich data
• Prototype uses secure
communication over
Bluetooth
Application Layer
Application(s)
Context Enr ichment Layer
ContextGReconciliation
InconsistencyG
Resolving
Integration
ContextGAcquisition
ContextG
Manager
Ontology
Reasoner
ContexualGGroupGManager
GGPrimaryGContext
SecondaryGContext
Communication
Context Gener ation Layer
ContextGSynthesizer(s)
ContextGProvider(s)
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
Context integration and reconciliation
• coments
Faceblock
Click image to play 80 second video or go to Youtube
http://ebiq.org/p/666
Conclusion
• 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
http://ebiq.org/r/363