_______________THONG M. DOANHAN N. DINHNAM T. NGUYENPHUOC T. TRAN

LOFLocation Obfuscation Framework for Training-Free Localization

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Related Works

LOF

Conclusion

Introduction

Contents

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INTRODUCTION

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LOF is a security framework protecting privacy for SIL and other training-free localization algorithms. SIL: Search-based Indoor Localization Training-free: no need pre-built map for

localization save resources (human labor, time, money)

Why SIL needs protection?

Introduction

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RELATED WORKS

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SILTraining-Free Localization

SSID listKG MECH BranchReliance TrendsNMDC Head Office

URL listwww.kgmech.com/www.tiendeo.in/Shops/hyderabad/reliance-trendswww.nmdc.co.in/

Potential address list

• Khanij Bhavan, Masab Tank, Hyderabad – 500028

• 10-3-310/1, Masab Tank, Mehdipatnam, Hyderabad – 500028

• 1-10-39 to 44, Begumpet, Hyderabad, AP-50001610-4/A/12/1 Masab Tank, Hyderabad – 500018

• …

Search Engine

query

SSIDScanning

Geo-InfoRetrieving

Address Processing component

10-3-310/1 Masab Tank, Hyderabad, 500028

Masab Tank Road

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SILFramework

Address Processing

• Evaluate & Rank Addresses

Geo-Info Retrieving

• Search Engine

• Crawl Webs & Retrieve Geo-Info.

SSID Scanning

• Scan APs

• Pre-process APs

SSIDSCANNING

GEO-INFORETRIEVING

ADDRESSPROCESSING

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Accuracy: ~80% (1 km error-range)Time response: 1 min (acceptable for

indoor movement)Bandwidth cost: ~2MB per locationGeo-Retrieving component consumes

much bandwidth & time Solution: crowd-sourcing (cloud) to share geo-

info between users Result: negligible cost (2.5KB & 1 second per

location)

SILOverview Result

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Ask third-party for geo-info: Location privacy threat Leakage of user location information while asking for

geo-information through the cloud (third-parties, …)

Geo-Info

Third-Party

Geo-Info

SIL

User Location

device

User

SSID set

SILProblem ???

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LOFLOCATION OBFUSCATION FRAMEWORK

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K-Anonymity: Anonymize information Add distortion information in the query sent to

the third-partyPIH – Partial Information Hiding:

Reduce amount of actual information exposed to third-party

LOFApproach

Preserve the location anonymityKeeping the bandwidth cost at acceptable level

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Idea: Add K-1 users’ info

to disguise actual user’s info

Trusted anonymizer

LOFK-Anonymity

Apply: No anonymizer Add disguised SSIDs to the query sent to

third-party

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LOFApproach

originalset

requestset

disguisedset

PIH

K-Anonymity

Third-Party

obfuscatedset

Geo-Info

requestset

self-processsetself-process

set

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LOFParameters

originalset

requestset

α

disguisedset

β

α 100%: bandwidth is negligible

since the whole original set is queried

α increase anonymity decreaseβ

200%: means disguised SSIDs are two times more than original set

β increase anonymity increase

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LOFDistribution of Disguised SSIDs

RD – Random Distribution:The SSIDs are scattered randomly and have no geo-relation with each other.

ID – Inter-proximate Distribution:The SSIDs are geo-correlated and in close proximity with each other.

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LOFEffect of α and β on Anonymity and Overhead

α=50% β=100%: bandwidth reduced in halfα=100% β=100%: negligible bandwidthAnonymity in both cases is at least 90%

10 20 30 40 50 10060

70

80

90

100

β = 200%

β = 100%

β = 50%

β = 25%

β = 0%

α (%)

No

rmal

ized

An

on

ymit

y (%

)

Fixed β, error range = 500mwith ID SSIDs

10 20 30 40 50 10060

70

80

90

100

β = 200%

β = 100%

β = 50%

β = 25%

β = 0%

α (%)

No

rmal

ized

An

on

ymit

y (%

)

Fixed β, error range = 500mwith RD SSIDs

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LOFEffect of ID and RD distributions on Anonymity

ID is better in obfuscating data than RD due to geo-correlation attribute of CGSIL

Anonymity level with fixed α, error range = 500m

0 25 50 100 20060

70

80

90

100

ID, α=50%ID, α=100%RD, α=50%

β (%)

No

rma

lize

dA

no

ny

mit

y (

%)

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LOFCorrelation of α and β

Low values of β: the anonymity is dependent upon α’s valueHigh values of β: the anonymity is dependent upon β’s value

Hit-Rate of Third-Party Predictionwith β=0%

Hit-Rate of Third-Party Predictionwith β=200%

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CONCLUSION

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LOF efficiently keeps the bandwidth overhead of SIL at minimal level while offering 90% anonymity.

Parameters (α, β) are configurable:

CONCLUSION

α β Bandwidth Anonymity

50% 100% ½ reduced 90%

100% 100% Negligible 85%

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References1. Truc D. Le, Thong M. Doan, Han N. Dinh, Nam T. Nguyen, “ISIL: Instant Search-based Indoor

Localization”, in Conference “CCNC 2013- Mobile Device & Platform & Applications”, The 10th Annual IEEE CCNC, Las Vegas, NV, USA, 2013.

2. Thong M. Doan, Han N. Dinh, Nam T. Nguyen, “CGSIL: Collaborative Geo-clustering Search-based Indoor Localization”. Accepted in the 16th IEEE International Conference on High Performance Computing and Communications (HPCC), Paris, France, 2014

3. Han N. Dinh, Thong M. Doan, Nam T. Nguyen, “CGSIL: A Viable Training-Free Wi-Fi Localization”, in the Eighth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM), Rome, Italy, 2014.

4. L. Sweeney: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems (2002) 557-570

5. Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, and Dimitris Papadias: Preventing Location-Based Identity Inference in Anonymous Spatial Queries, Vol 19, No. 12. IEEE Transactions on Knowledge and Data Engineering (12-2007) 1719-1733

6. Buğra Gedik, Ling Liu: A Customizable k-Anonymity Model for Protecting Location Privacy. ICDCS (2004) 620–629

7. Ge Zhong, Urs Hengartner: A Distributed k-Anonymity Protocol for Location Privacy. IEEE Int. Conference on Pervasive Computing and Communications (PerCom) (2009) 1-10

8. Buğra Gedik, Ling Liu: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms, Vol. 7, No. 1. IEEE Transactions on Mobile Computing (2008)

9. Aris Gkoulalas–Divanis, Panos Kalnis, Vassilios S. Verykios: Providing K–Anonymity in Location Based Services, Vol. 12, Issue 1. SIGKDD Explorations

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Q&A