Real-Time Aggregate Monitoring with Differential …...State-of-the-art: Discrete Fourier Transform...
Transcript of Real-Time Aggregate Monitoring with Differential …...State-of-the-art: Discrete Fourier Transform...
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Real-Time Aggregate Monitoring with
Differential Privacy
Li Xiong
Department of Mathematics and Computer Science
Department of Biomedical Informatics
Emory University
(Joint work with Liyue Fan, Vaidy Sunderam)
iDASH Privacy Workshop 2012
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Scenario
• Disease Surveillance
• E.g. daily count of flu
cases in different
regions
• Traffic Monitoring
• E.g. hourly count of
vehicles at different
intersections
• Single time-series
• Multi-dimensional
time-series
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Single Time-Series: Problem Statement
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Utility
k-1 k time
• Point-wise: average relative error
• Time-series: outbreak detection
• Outbreak at time k: xk – xk-1 >= threshold
• Specificity and sensitivity
• Precision and recall: F1 metric
R
X
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Baseline: Laplace Perturbation Algorithm (LPA)
Laplace Perturbation k time
At each time point
xk
Aggregate time-series X
rk
Released time-series R
k time • High sensitivity : O(T)
• High perturbation error: O(T)
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State-of-the-art: Discrete Fourier Transform
[RN10]
Laplace Perturbation
Aggregate series X
Released series R
Discrete Fourier
Transform
Inverse DFT
Retain only the first
l coefficients to
reduce sensitivity
• Higher accuracy
• Offline or batch processing only
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FAST: Filtering and Adaptive Sampling for
aggregate Time-series monitoring
• Filtering – posterior estimate based on prediction and perturbed values
• Adaptive sampling - reduce sensitivity
Laplace
Perturbation
Filtering
Prediction
Correction
Adaptive
Sampling
error
sampling
rate
Time-series output Sampling
point
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Filtering: State-Space Model
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Process noise
Measurement noise
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Filtering: Posterior Estimation
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Filtering: Solutions
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Adaptive Sampling
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• Fixed sampling – difficult to select sampling rate a priori
• Adaptive sampling - adjust sampling rate based on feedback from
observed data dynamics
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Adaptive Sampling: PID Control
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Some results: average relative error
• Flu dataset (CDC): weekly outpatient count of age group [5-24] from
2006 – 2010 (209 data points)
• Traffic dataset (U Washington): daily traffic count for Seattle-area
highway at I-5 143.62 southbound from 2003 – 2004 (504 data points)
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Some Results: F1 metric for outbreak detection
traffic data set
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Multi-dimensional time-series: Problem
statement
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Multi-dimensional time-series: challenges and
solutions
• Data has sparse and
uniform regions
• Data is dynamically
changing
Group similar
cells
Reorganize groups
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FAST with Partitioning
Laplace
Perturbation
Filtering
Prediction
Correction
Adaptive
Sampling
error
sampling
rate
update
multi-dimensional
Time-series output
Dynamic
Partitioning
partitioning
keys
sampling
partitions
• Dynamic spatial partitioning: based on KD-Tree and quad-tree
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Some Results
Synthetic traffic data by Brinkhoff
moving objects generator
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Closing Remarks
• Utility: time point wise relative error still high but can be
useful for time-series driven applications such as outbreak
detection
• Key insight: feedback loops are useful to dynamically
adjust sampling, aggregation, and estimation
• Open question: how to allocate budget over time points?
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Thank you
• References
• Liyue Fan, Li Xiong. Real-Time Aggregate Monitoring with
Differential Privacy, CIKM 2012
• Liyue Fan, Li Xiong. Adaptively sharing time-series with differential
privacy, arXiv:1202.3461, 2012
• Research Support Acknowledgement
• AFOSR: PREDICT: Privacy and Security Enhancing Dynamic
Information Collection and Monitoring
• NSF: Adaptive Differentially Private Data Release
• Contact
• AIMS: Assured Information Management and Sharing
http://www.mathcs.emory.edu/aims