This document discusses using contextual information like location and habitual metrics to improve biometrics for authentication. It notes that location data is less invasive than habitual data. The objectives are to understand how context provides better biometrics usage and how privacy is affected. Challenges include privacy concerns, data processing, and sensor accuracy. A literature review found a user's identity comes from who they are, what they request, how/when/where they connect, and why. The solution is to combine data from multiple sensors like GPS, Bluetooth, and accelerometers. Benefits for enterprises include dynamic security and understanding employee identity and habits. Next steps include privacy best practices, prototyping, and testing metrics. An internship would allow exploring relating biome

1. Biometrics and Context

2. OUR TEAM
• Morgan Mayernik
• David Stroh
• Zach Moore
• Torrey Hutchison

3. Biometrics plus contextual
information is appropriate given
the performance requirements
in the workplace.
Location and habitual metrics
are currently the most useful for
the enterprise; however location
data is less invasive than
habitual data.
HYPOTHESIS
Key Takeaway: location and habit metrics
1

4. PROJECT OBJECTIVES
• How can context provide better biometrics usage?
• How should security and convenience be measured,
and how does a contextual system affect both
variables?
• How does each contextual metric contribute to
authenticating a user, and which are most effective
when combined together?
• How does privacy legislation and public opinion affect
the collection of contextual data?
Key Takeaway: There is a lot of big data that can build
context, and metrics should build upon one another
towards verifying a user’s identity.

5. CHALLENGES
• User acceptance of biometrics and privacy
concerns
• Contextual metrics are more invasive than
traditional methods of authentication
• Machine Learning may not be capable of
managing shifting multi-context situations
• Processing the massive amounts of contextual
data collected is overwhelming
• Current sensor technology and data collection
techniques may not be sufficiently accurate
2

6. LITERATURE REVIEW FINDINGS
(1)
• Who the user is
• What the user is requesting
• How the user is connected
• When the user is connecting
• Where the user is connecting from
• Why is the user connecting
(2)
Key Takeaway: A user’s
virtual identity is comprised
of a variety of factors
3,4,5

7. Our Solution
Device Attributes
1. GPS
2. Bluetooth
3. Camera
4. Gyroscope
5. Accelerometer
6. Microphone
DataType
Location
Movement
Noise
Light
Video
Insights
Identity
Locational
Temporal
Behavioral
Habitual
Social
EnvironmentalKey Takeaway: Combining multiple forms
of sensors and data will increase security

8. BENEFITS IN THE ENTERPRISE
• Integration between work calendars and authentication
• Dynamic security
• Increases understanding of employees’ workplace “identity”
based on habits and other contextual information
• Potential to use contextual data for other purposes, such as
office productivity
Key Takeaway: Continuous Authentication and
Contextual Data increases security for the Enterprise
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9. NEXT STEPS
• Contextual data collection privacy best practices document
• Model the contextual data collection/analysis process
• Develop prototypes for using contextual information to
increase security
• Test contextual metrics for privacy, security, and entropy
• Examine the role IOT plays in contextual data
Key Takeaway: Privacy, IOT, Mobile, Big Data, Prototyping.
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10. INTERNSHIP
•An internship this summer will allow more time to
explore the possibility of relating biometrics with
contextual metrics within IOT.
•We would also have the opportunity to move
beyond literature review into more polished testing.
•We would gain a better understanding of the
enterprise environment, and what contextual data
is most available for widespread usage.

11. REFERENCES
1. http://androidphonehub.com/huawei/test-huawei-p8-lite-lightweight-
version/attachment/huawei-p8-lite-gps-data/
2. http://www.fastcodesign.com/1672531/the-future-of-technology-isnt-mobile-its-
contextual
3. https://www.signup4.net/Upload/TERA10A/20142362E/3-T2-6-
Pacific%20Northwest%20National%20Laboratory-Pike.pdf
4. http://www.bytes.co.uk/files/7313/4383/1104/Gartner_Reprint-
_The_Future_of_Information_Security_is_Context_Aware_and_Adaptive.pdf
5. https://www.signup4.net/Upload/TERA10A/20142362E/3-T2-6-
Pacific%20Northwest%20National%20Laboratory-Pike.pdf
6. https://community.rackspace.com/general/f/34/t/1627
7. http://mitco.me/post/119582601970/age-of-context