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Wireless Location Privacy: Radiometric Breaches and Defenses
Marco Gruteser WINLAB
Trends
Always-on, transmitting background apps Low-cost software (controlled) radios (e.g., GNU radio)
Wireless Location Privacy: Radiometric Breaches and Defenses Marco - - PowerPoint PPT Presentation
Wireless Location Privacy: Radiometric Breaches and Defenses Marco - - PowerPoint PPT Presentation
Wireless Location Privacy: Radiometric Breaches and Defenses Marco Gruteser WINLAB Trends Always-on, transmitting Low-cost software (controlled) radios background apps (e.g., GNU radio) Removing Identifiers at or above Bit-Level: Example
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Removing Identifiers at or above Bit-Level: Example
Probe Vehicles GPS Satellite Traffic Estimation Data mining and logging Cellular Service Provider Vehicle ID | timestamp | Lon | Lat | Speed | Heading
- 254,18-oct-2006 10:11:12,-85.3452,42.4928,42.18,135
372,18-oct-2006 10:11:12,-85.3427,42.4898,63.72,100 182,18-oct-2006 10:11:12,-85.4092,42.4726,50.15,75 254,18-oct-2006 10:12:12,-85.3462,42.4998,45.18,135 372,18-oct-2006 10:12:12,-85.3512,42.4944,60.01,185 182,18-oct-2006 10:12:12,-85.4102,42.4753,45.88,235 … 254,18-oct-2006 10:21:12,-85.3856,42.5129,45.67,135 Location Proxy
Access Control Anonymization
Anonymous Trace log files
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Privacy Im plications at the Signal Level?
- Possible Breaches
– Identify transmitters? – Infer information
- ther than location?
- Protections
– Prevent long-term tracking? – Thwart localization? Beyond Localization …
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Recent Controversy about Human Mobility Article Demonstrates Location Privacy Sensitivities
- Analyzed human mobility
patterns from cell phone hand
- ff data
– 100,000 users over 6 months
- Coarse data
– Cell tower location recorded for each call / message – Average towers covered 3 km^ 2
- Example of typical
assumptions about location inference from signals
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Identifying Transmitters via Radiometric Signatures
with Suman Banerjee (MobiCom’08)
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Waveform Impairments in Analog Frontend
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Transmitter Identification via Classification
- Training phase: collect fingerprint (waveform error metrics)
- f each transmitter
- Identification phase: measure error metrics for candidate
transmitter and use classification algorithm to match with training set
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ORBIT
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Identification Results
- Using ORBIT testbed radios and vector signal analyzer for
data collection
- K-Nearest Neighbor and Support Vector Machine classifiers
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Identifying Co-location (While Moving) without Location
With Rich Martin, Yingying Chen (MASS 08)
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Motivating Applications
- Co-location can reveal
social relationships among device owners
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Properties of Co-Mobile Transmitters
Fast fading differs but slow fading similar
Small scale fading
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Non- Co-Mobile Transmitters
Both large and small scale fading differs
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Detection via Filtering and Time- Series Correlation
Pearson’s Product Moment Correlation Coefficient:- Determines the Linear Relationship between two Random Variables (Observed RSSI from 2 Devices) Ranges between [-1, +1]
– : No Correlation – +1 : Strong Positive Correlation –
- 1 : Strong Negative Correlation
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Experimental Setup
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Impact of Speed on Co-Movement Detection Time
Correlation-Coefficient Threshold >= 0.6 Conclusion: 50-100 Sec of Observation Required.
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Thwarting Tracking: Applying Path Cloaking
with Hui Xiong (CCS 07)
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Inference/ Insider Attacks Compromise Location Privacy
Still insider breaches and remote break-ins possible Re-identification
- f traces
through data analysis
Home Identification [Hoh06] Tracking algorithms recover individual trace [Hoh05] (Median trip time only 15min)
. . . . . . . . . . . . . . . .
Anonymous Trace log files Location may be precise enough to identify home
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Stronger De-identification for Location Traces: Filtering based on Tracking Model
t=T1 t=T2
Low Entropy Low Uncertainty
t=T2 t=T2
High Entropy High Uncertainty
d1 d2 d3
t=T1 t=T2 t=T2 t=T2
Normalization
d1 d2 d3
Uncertainty
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Thwarting Precise Localization
Preliminary Work
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Power Control?
(X1,Y1) 3db
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Power Control Provides Little Benefit
(X1,Y1) 3db 3db 3db 3db
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Cooperation can yield Asymmetric Signal Changes
0db 3db 1db
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Proof-of-Concept Result
4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Grid − x Grid − y Victim Node Cooperator−1 Decoy Location −1 6dBm 1dBm 12dBm 4 5 6 7 8 9 10 11 12 13 14 15 16 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Grid − x Grid − y Victim Node Cooperator−2 Decoy Location−2 6dBm 1dBm 12dBm
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Summary
0db 3db 1db
d1 d2 d3
t=T1 t=T2 t=T2 t=T2
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