research-article

Inferring social ties in academic networks using short-range wireless communications

Authors:

Igor Bilogrevic,

Kévin Huguenin,

Murtuza Jadliwala,

Jean-Pierre Hubaux,

Philip Ginzboorg,

Valtteri NiemiAuthors Info & Claims

WPES '13: Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society

Pages 179 - 188

https://doi.org/10.1145/2517840.2517842

Published: 04 November 2013 Publication History

Abstract

WiFi base stations are increasingly deployed in both public spaces and private companies, and the increase in their density poses a significant threat to the privacy of connected users. Prior studies have provided evidence that it is possible to infer the social ties of users from their location and co-location traces but they lack one important component: the comparison of the inference accuracy between an internal attacker (e.g., a curious application running on a mobile device) and a realistic external eavesdropper in the same field trial. In this paper, we experimentally show that such an eavesdropper is able to infer the type of social relationships between mobile users better than an internal attacker. Moreover, our results indicate that by exploiting the underlying social community structure of mobile users, the accuracy of the inference attacks doubles. Based on our findings, we propose countermeasures to help users protect their privacy against eavesdroppers.

References

[1]

I. Aad, M. Jadliwala, I. Bilogrevic, V. Niemi, J. Hubaux, P. Ginzboorg, and K. Leppänen. Nokia Instant Community at EPFL: A Real-World Large-Scale Wireless Peer-to-Peer Trial. Technical Report 170421, EPFL, 2011.

[2]

H. Akaike. A New Look at the Statistical Model Identification. IEEE TAC, 19:716--723, 1974.

[3]

J. Banford and J. Irvine. Estimating social graphs in an education environment. IEEE VT Mag., 7(1), 2012.

[4]

A. Beresford and F. Stajano. Location Privacy in Pervasive Computing. IEEE Perv. Comp., 2, 2003.

Digital Library

[5]

I. Bilogrevic, M. Jadliwala, I. Lám, I. Aad, P. Ginzboorg, V. Niemi, L. Bindschaedler, and J. Hubaux. Big brother knows your friends: on privacy of social communities in pervasive networks. Pervasive, pages 370--387, 2012.

Digital Library

[6]

L. Bindschaedler, M. Jadliwala, I. Bilogrevic, I. Aad, P. Ginzboorg, V. Niemi, and J. Hubaux. Track me if you can: On the effectiveness of context-based identifier changes in deployed mobile networks. In NDSS, 2012.

[7]

S. P. Borgatti, A. Mehra, D. J. Brass, and G. Labianca. Network Analysis in the Social Sciences. Science, 323:892--895, 2009.

[8]

A. Bose and C. Foh. A Practical Path Loss Model for Indoor WiFi Positioning Enhancement. In ICICS, 2007.

[9]

D. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring Social Ties from Geographic Coincidences. Proc. Nat. Ac. Sc., 107:22436--22441, 2010.

[10]

C. Diot. Haggle Project. http://www.haggleproject.org/. Last visited 13/Mar/2013.

[11]

N. Eagle and A. Pentland. Reality mining: sensing complex social systems. Pers. Ubiq. Comp., 10:255--268, 2006.

Digital Library

[12]

N. Eagle, A. Pentland, and D. Lazer. Inferring Friendship Network Structure by Using Mobile Phone Data. Proc. Nat. Ac. Sc., 106:15274--15278, 2009.

[13]

Femto Forum. http://femtoforum.org/fem2/pressreleases.php?id=277, Last visited 13/Mar/2013.

[14]

FON. http://corp.fon.com/en. Last visited 13/Mar/2013.

[15]

S. Fortunato. Community Detection in Graphs. Physics Reports, 486, 2010.

[16]

Map of FreeWifi Hotspots. http://iphone.r0ro.fr/fwConnect/map.php, Visited on 01/Nov/2012.

[17]

N. Gong, M. Laibowitz, and J. Paradiso. Dynamic Privacy Management in Pervasive Sensor Networks. In Proc. Int. Joint Conf. on Ambient Intell., 2010.

Digital Library

[18]

B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady. Enhancing Security and Privacy in Traffic-Monitoring Systems. IEEE Perv. Comp., 5:38--46, 2006.

Digital Library

[19]

H. Hu, S. Myers, V. Colizza, and A. Vespignani. Wifi networks and malware epidemiology. Proc. Nat. Ac. Sc., 106(5):1318, 2009.

[20]

N. Kiukkonen, J. Blom, O. Dousse, D. Gatica-Perez, and J. Laurila. Towards Rich Mobile Phone Datasets: Lausanne Data Collection Campaign. In ICPS, 2010.

[21]

Y. Matsuo, N. Okazaki, K. Izumi, Y. Nakamura, T. Nishimura, and K. Hasida. Inferring Long-term User Property based on Users. In IJCAI, 2007.

Digital Library

[22]

A Definitive List of NFC Phones. http://www.nfcworld.com/nfc-phones-list/, Last visited 13/Mar/2013.

[23]

T. Nicolai, E. Yoneki, N. Behrens, and H. Kenn. Exploring Social Context with the Wireless Rope. In OTM, 2006.

Digital Library

[24]

G. Palla, I. Derényi, I. Farkas, and T. Vicsek. Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature, 435, 2005.

[25]

Qualcomm AllJoin. https://developer.qualcomm.com/develop/mobile-technologies/peer-peer-alljoyn, Last visited 13/Mar/2013.

[26]

SFR Carte Wifi. http://cartewifi.sfr.fr, Last visited 13/Mar/2013.

[27]

P. Traynor, K. Butler, W. Enck, P. McDaniel, and K. Borders. Malnets: Large-Scale Malicious Networks via Compromised Wireless Access Points. Security and Communication Networks, 3(2):102--113, 2010.

[28]

E. Yoneki, D. Greenfield, and J. Crowcroft. Dynamics of Inter-Meeting Time in Human Contact Networks. In Proc. Int. Conf. on Adv. in Soc. Net. Analysis and Mining, 2009.

Digital Library

Cited By

Jin FHua WFrancia MChao POrlowska MZhou X(2022)A Survey and Experimental Study on Privacy-Preserving Trajectory Data PublishingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3174204(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3174204
Murakami THamada KKawamoto YHatano T(2021)Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific FeaturesProceedings on Privacy Enhancing Technologies10.2478/popets-2021-00152021:2(5-26)Online publication date: 29-Jan-2021
https://doi.org/10.2478/popets-2021-0015
Chen ZLu JWang R(2020)A New Algorithm for Social Inference Using Position Information2020 International Conference on Big Data and Informatization Education (ICBDIE)10.1109/ICBDIE50010.2020.00103(413-416)Online publication date: Apr-2020
https://doi.org/10.1109/ICBDIE50010.2020.00103
Show More Cited By

Index Terms

Inferring social ties in academic networks using short-range wireless communications

Recommendations

Inferring social ties in pervasive networks: an on-campus comparative study
UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

WiFi base stations are increasingly deployed in both public spaces and private companies, and the increase in their density poses a significant threat to the privacy of users. Prior studies have shown that it is possible to infer the social ties between ...
Inferring private information using social network data
WWW '09: Proceedings of the 18th international conference on World wide web

On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is ...
Exploiting Users' Inconsistent Preferences in Online Social Networks to Discover Private Friendship Links
WPES '14: Proceedings of the 13th Workshop on Privacy in the Electronic Society

In a social network system, a friendship relation between two users is usually represented by an undirected link and it is visible in both users' friend lists. Such a dual visibility of a friendship link may raise privacy threats. This is because both ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WPES '13: Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society

November 2013

306 pages

ISBN:9781450324854

DOI:10.1145/2517840

General Chair:
Ahmad-Reza Sadeghi
TU-Darmstadt & Fraunhofer SIT & Intel ICRI-SC, Germany
,
Program Chair:
Sara Foresti
Università degli Studi di Milano, Italy

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CCS'13

Sponsor:

SIGSAC

CCS'13: 2013 ACM SIGSAC Conference on Computer and Communications Security

November 4, 2013

Berlin, Germany

Acceptance Rates

WPES '13 Paper Acceptance Rate 30 of 103 submissions, 29%;

Overall Acceptance Rate 106 of 355 submissions, 30%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
182
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jin FHua WFrancia MChao POrlowska MZhou X(2022)A Survey and Experimental Study on Privacy-Preserving Trajectory Data PublishingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3174204(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3174204
Murakami THamada KKawamoto YHatano T(2021)Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific FeaturesProceedings on Privacy Enhancing Technologies10.2478/popets-2021-00152021:2(5-26)Online publication date: 29-Jan-2021
https://doi.org/10.2478/popets-2021-0015
Chen ZLu JWang R(2020)A New Algorithm for Social Inference Using Position Information2020 International Conference on Big Data and Informatization Education (ICBDIE)10.1109/ICBDIE50010.2020.00103(413-416)Online publication date: Apr-2020
https://doi.org/10.1109/ICBDIE50010.2020.00103
Khalfoun BMaouche MBen Mokhtar SBouchenak S(2019)MooDProceedings of the 20th International Middleware Conference10.1145/3361525.3361542(136-148)Online publication date: 9-Dec-2019
https://dl.acm.org/doi/10.1145/3361525.3361542
Humbert MTrubert BHuguenin K(2019)A Survey on Interdependent PrivacyACM Computing Surveys10.1145/336049852:6(1-40)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3360498
Primault VBoutet AMokhtar SBrunie L(2019)The Long Road to Computational Location Privacy: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2018.287395021:3(2772-2793)Online publication date: Nov-2020
https://doi.org/10.1109/COMST.2018.2873950
Cerf SBouchenak SRobu BMarchand NPrimault VBen Mokhtar SBoutet AChen L(2018)Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based ApproachIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2018.2884470(1-1)Online publication date: 2018
https://doi.org/10.1109/TDSC.2018.2884470
Cerf SBen Mokhtar SBouchenak SMarchand NRobu B(2018)Dynamic Modeling of Location Privacy Protection MechanismsDistributed Applications and Interoperable Systems10.1007/978-3-319-93767-0_3(26-39)Online publication date: 1-Jun-2018
https://doi.org/10.1007/978-3-319-93767-0_3
Maouche MMokhtar SBouchenak SGu TKotagiri RLiu H(2017)AP-AttackProceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3144457.3144494(48-57)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3144457.3144494
Wang FZhu XMiao J(2017)Semantic trajectories-based social relationships discovery using WiFi monitorsPersonal and Ubiquitous Computing10.1007/s00779-016-0983-z21:1(85-96)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00779-016-0983-z
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents