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Inference of User Demographics and Habits from Seemingly BenignSmartphone Sensors
Manar Safi, Irwin Reyes, Serge EgelmanUniversity of California – Berkeley & International Computer Science Institute
Introduction
Smartphone permissions systems control access to private user data. [1,2]
Non-GPS sensors are not restricted.
The ad-tech industry builds individual profiles to better target ads [4] We explore methods to infer private data from these “benign” sensors.
Objectives
Research Question: Are sensor readings correlated with user traits? Is it sufficient for machine learning?
Methodology
Data Collection:Population: MTURK, 1-week observation period, N = 100Preprocess, segment, and classify
Preliminary Results
1. Requesting Permission - Interaction - iOS Human Interface Guidelines. https://developer.apple.com/ios/human- interface guidelines/interaction/requesting-permission/
2. Working with System Permissions — Android Developers. https://developer.android.com/training/permissions/index.html
3. Sensor types — Android Open Source Project. https://source.android.com/devices/sensors/ sensor-types.html.
4. How much is your personal data worth? - FT.com. http://www.ft.com/cms/s/2/927ca86e-d29b-11e288ed-00144feab7de.html#axzz4BDh1fipu
5. Y. Michalevsky, A. Schulman, G. A. Veerapandian, D. Boneh, and G. Nakibly. Powerspy: Location tracking using mobile device power analysis. In 24th USENIX Security Symposium (USENIX Security 15), pages 785–800, Washington, D.C., Aug. 2015. USENIX Association.
6. E. Owusu, J. Han, S. Das, A. Perrig, and J. Zhang. Accessory: Password inference using accelerometers on smartphones. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, HotMobile ’12, pages 9:1–9:6, New York, NY, USA, 2012. ACM.
Conclusion
Key Takeaways• Sensors can correlate with one
another under various activities
• Conditioned on events, inferences about the user can be made
• Needs further investigation to determine generalizability
Questions For Further Analysis• For gender inference using
walking motion, how stable are the features within genders and across different walking sessions?
• What are the best ways to preprocess, segment, and formulate features from rich sensor data for demographic classification?
• Are there systemic differences in handset sensor hardware that can bias data and resulting inferences? Can those be leveraged?
References
Intuition on Sensor Measurements
• Motion sensor activity correlates with one another (e.g., step counter activity reflected in accelerometer)
• Sensor activity + times could indicate habits, changes in location, etc.
Walking Motion and Gender• Intuition: Men and women store their
phones differently when walking (e.g., side pocket vs. back pocket vs. hand bag)
• Intuition: Different storage will have distinct motion characteristics
• Step counter data used to condition accelerometer readings to walking
• Limited analysis shows possible distinguishable features in time and frequency domains
• Needs broader investigation and sensitivity analysis
Gender Weight Fitness Habits
Income level Height Work Schedule
Ground TruthSensor Data
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21:38:22 21:38:22 21:38:23 21:38:24 21:38:25 21:38:26
AccelerometerL2
Norm(m
/s2 )
Time
FemaleWalking(5SecondDetail)
0
500
1000
1500
2000
2500
3000
0 5 10 15 20 25
PeakAmplitu
de
Frequency(Hz)
FemaleWalking,FFT4096Pts.@50Hz
051015202530354045
10:15:10 10:15:10 10:15:11 10:15:12 10:15:13 10:15:14
AccelerometerL2
Norm(m
/s2 )
Time
MaleWalking(5SecondDetail)
0
500
1000
1500
2000
2500
3000
0 5 10 15 20 25
PeakAmplitu
de
Frequency(Hz)
MaleWalking,FFT4096Pts.@50Hz
Accelerometer LightSensors
StepCounter
ProximitySensor Gyroscope
Contacts Photos &Videos
FineLocation
CallActivity SMS