preserving location privacy on the release of large-scale mobility data xueheng hu , aaron d. ...
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Preserving Location Privacy on the Release of Large-scale Mobility Data Xueheng Hu , Aaron D. Striegel Department of Computer Science and Engineering University of Notre Dame. Introduction. Mobility models for wireless networks simulation . Synthetic models: Easy to set up - PowerPoint PPT PresentationTRANSCRIPT
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Preserving Location Privacy on the Release of Large-scale
Mobility Data
Xueheng Hu, Aaron D. StriegelDepartment of Computer Science and Engineering
University of Notre Dame
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IntroductionMobility models for wireless networks simulation Synthetic models:
• Easy to set up• Inadequate semantics to capture reality
Traces: • Observed in real life, expected to be accurate• Expensive to collect, privacy concerns
Challenge: trace publishing vs. location privacy
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PreviewTrade-off: data utility vs. user privacy
Simulation scenariosDTN, Opportunistic N/W, etc.
Focusing on realistic wireless interactions - utilityFreeing from absolute locations - privacy
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Computational Efforts• Anonymization
Creating ambiguity, e.g. k-anonymity (Sweeney, 2002)
User A User B
User CUser A, B, and
C
Anonymization
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Computational Efforts• Obfuscation
Degrading data quality (Krumm, 2009)
(a) Original GPS data (b) Adding Gaussian noise
(c) Discretizing data to a grid
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Data Source - NetSense Study
Provided 200 smart devices to incomingfreshmen at Notre Dame (Aug 2011)
200x Nexus S 4G200 anytime minsUnlimited data,
text User level agent
(1-3 min polling intervals)Environment (WiFi, Cell)User proximity (Bluetooth)Phone state (Screen, Battery)Phone usage (data / app tonnage)
802.11n, 802.11g
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Notations
• MN = {MN1, MN2, MN3}
• AP = {AP1, AP2, AP3}
• NLit (WiFi Proximity Set), e.g., NL1
t = {AP1, AP2, AP3}
• NSit (B/T Proximity Set), e.g., NSi
t = {MN2, MN3}
• RL: WiFi radio range• RS
: B/T radio rangeThe vision of MN1 at
time t
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ApproachProblem Description
Input: wireless relationships (WiFi and B/T proximity)
constrained by RL and RS Output: trace solution for each mobile nodepreserving α of the given wireless relationships
Algorithm Components Step 1: Access Point Deployment (2D)Step 2: Mobility Trace Generation
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AP DeploymentConnectivity Graph• Connected if detected by the same mobile device
• Infer AP-to-AP relative distances (shortest path)
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AP Deployment
CMDS
Classic Multidimensional Scaling (Torgerson, 1952)
(Source: https://personality-project.org/r/mds.html)
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Trace GenerationAP deployment as the input
Location of a mobile device depends on locations of detected APs and MNs
Multiple solutions provide ambiguityp1, p2, p3 – candidate solutionsfor mobile node x
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Evaluation MetricsBluetooth proximity preservation w.r.t. RS o Precision: PNS
o Recall: RNS
o F_Score: FNS
N
i
T
tti
ti
ti
NS TNNSretrieved
NSretrievedNSoriginalP1 1
)/()|_|__(
N
i
T
tti
ti
ti
NS TNNSoriginal
NSretrievedNSoriginalR1 1
)/()|_|__(
NSNS
NSNSNS RP
RPF
2
WiFi proximity preservation w.r.t. RL (similar)
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Results
Bluetooth Proximity Preservation - Aggregate
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Results
WiFi Proximity Preservation - Aggregate
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Results F_Score Distribution: Bluetooth vs. WiFi proximity
preservation
WiFi Proximity:~80% nodes ≥
0.78 B/T Proximity:~95% nodes ≥
0.90
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Results Inter-contact Time Distribution: Original samples vs. Generated
traces
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SummaryA novel approach to preserve location privacy using zero knowledge of actual locationsPotential impact where traces are needed but not availableMetrics to evaluate the preservation of wireless relationships
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Open ProblemsIntroducing RSSI for better hint to relative distances3D space deployment
Security challenges (quantitatively measuring how much privacy gained)
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Questions?
Contact InformationProf. Aaron Striegel : [email protected]
Xueheng Hu: [email protected]
NetSense Study: http://netsense.nd.edu/