skip to main content
10.1145/2872427.2883015acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

Strengthening Weak Identities Through Inter-Domain Trust Transfer

Published: 11 April 2016 Publication History

Abstract

On most current websites untrustworthy or spammy identities are easily created. Existing proposals to detect untrustworthy identities rely on reputation signals obtained by observing the activities of identities over time within a single site or domain; thus, there is a time lag before which websites cannot easily distinguish attackers and legitimate users. In this paper, we investigate the feasibility of leveraging information about identities that is aggregated across multiple domains to reason about their trustworthiness. Our key insight is that while honest users naturally maintain identities across multiple domains (where they have proven their trustworthiness and have acquired reputation over time), attackers are discouraged by the additional effort and costs to do the same. We propose a flexible framework to transfer trust between domains that can be implemented in today's systems without significant loss of privacy or significant implementation overheads.
We demonstrate the potential for inter-domain trust assessment using extensive data collected from Pinterest, Facebook, and Twitter. Our results show that newer domains such as Pinterest can benefit by transferring trust from more established domains such as Facebook and Twitter by being able to declare more users as likely to be trustworthy much earlier on (approx. one year earlier).

References

[1]
http://oauth.net.
[2]
http://klout.com/home.
[3]
https://trustcloud.com.
[4]
Advogato trust metric. http://www.advogato.org/trust-metric.html.
[5]
Black-market for old twitter accounts. http://buybulkaccounts.blogspot.de/p/5-years-old.html.
[6]
Black-market website. http://addmefast.com.
[7]
Black-market website. http://www.purchasesocial.com.
[8]
Black-market website. https://socioblend.com.
[9]
Facebook anonymous login. http://newsroom.fb.com/news/2014/04/f8-introducing-anonymous-login-and-an-updated-facebook-login.
[10]
Facebook login. https://developers.facebook.com/docs/facebook-login/v2.3.
[11]
Pinterest introductory guide. https://help.pinterest.com/en/guide/all-about-pinterest.
[12]
Reddit faq. http://www.reddit.com/r/help/wiki/faq.
[13]
Social login market. http://www.gigya.com/blog/the-landscape-of-social-login-facebook-makes-a-comeback.
[14]
Tech blog post about facebook integration. http://royal.pingdom.com/2012/06/18/how-many-sites-have-facebook-integration-youd-be-surprised.
[15]
Web of trust. https://www.mywot.com.
[16]
F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting spammers on twitter. In CEAS'10.
[17]
A. Beutel, W. Xu, V. Guruswami, C. Palow, and C. Faloutsos. Copycatch: stopping group attacks by spotting lockstep behavior in social networks. In WWW'13.
[18]
Q. Cao, X. Yang, J. Yu, and C. Palow. Uncovering large groups of active malicious accounts in online social networks. In ACM CCS'14.
[19]
G. Danezis and P. Mittal. SybilInfer: Detecting Sybil Nodes Using Social Networks. In NDSS'09.
[20]
S. Feng, L. Xing, A. Gogar, and Y. Choi. Distributional footprints of deceptive product reviews. In AAAI ICWSM'12.
[21]
S. Ghosh, N. Sharma, F. Benevenuto, N. Ganguly, and K. Gummadi. Cognos: Crowdsourcing search for topic experts in microblogs. In ACM SIGIR'12.
[22]
S. Ghosh, M. B. Zafar, P. Bhattacharya, N. Sharma, N. Ganguly, and K. Gummadi. On sampling the wisdom of crowds: Random vs. expert sampling of the twitter stream. In ACM CIKM'13.
[23]
O. Goga, P. Loiseau, R. Sommer, R. Teixeira, and K. P. Gummadi. On the reliability of profile matching across large online social networks. In ACM KDD'15.
[24]
C. Grier, K. Thomas, V. Paxson, and C. M. Zhang. @spam: the underground on 140 characters or less. In ACM CCS'10.
[25]
T. Grinshpoun, N. Gal-Oz, A. Meisels, and E. Gudes. Ccr: A model for sharing reputation knowledge across virtual communities. In WI-IAT'09.
[26]
R. Heatherly, M. Kantarcioglu, and B. Thuraisingham. Preventing private information inference attacks on social networks. IEEE Trans. on Knowl. and Data Eng., 2013.
[27]
A. M. Kakhki, C. Kliman-Silver, and A. Mislove. Iolaus: Securing online content rating systems. In WWW'13.
[28]
A. Leontjeva, M. Goldszmidt, Y. Xie, F. Yu, and M. Abadi. Early security classification of skype users via machine learning. In AISec'13.
[29]
N. Li and T. Li. t-closeness: Privacy beyond k-anonymity and l-diversity. In IEEE ICDE'07.
[30]
E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting product review spammers using rating behaviors. In ACM CIKM'10.
[31]
M. McGlohon, N. S. Glance, and Z. Reiter. Star quality: Aggregating reviews to rank products and merchants. In AAAI ICWSM'10.
[32]
A. Mislove, A. Post, K. P. Gummadi, and P. Druschel. Ostra: Leveraging trust to thwart unwanted communication. In NSDI'08.
[33]
M. Mondal, B. Viswanath, A. Clement, P. Druschel, K. P. Gummadi, A. Mislove, and A. Post. Defending against large-scale crawls in online social networks. In ACM CoNEXT'12.
[34]
S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng., 2010.
[35]
A. Post, V. Shah, and A. Mislove. Bazaar: Strengthening user reputations in online marketplaces. In NSDI'11.
[36]
D. Quercia and S. Hailes. Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue. In IEEE INFOCOM'10.
[37]
T. Stein, E. Chen, and K. Mangla. Facebook immune system. In SNS'11.
[38]
K. Thomas, D. McCoy, C. Grier, A. Kolcz, and V. Paxson. Trafficking fraudulent accounts: The role of the underground market in twitter spam and abuse. In USENIX Security'13.
[39]
N. Tran, J. Li, L. Subramanian, and S. S. Chow. Optimal Sybil-resilient Node Admission Control. In IEEE INFOCOM'11.
[40]
N. Tran, B. Min, J. Li, and L. Subramanian. Sybil-resilient online content voting. In USENIX NSDI '09.
[41]
B. Viswanath, M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In USENIX Security'14.
[42]
B. Viswanath, M. A. Bashir, M. B. Zafar, S. Bouget, S. Guha, K. P. Gummadi, A. Kate, and A. Mislove. Strength in numbers: Robust tamper detection in crowd computations. In ACM COSN'15.
[43]
G. Wang, T. Konolige, C. Wilson, X. Wang, H. Zheng, and B. Y. Zhao. You Are How You Click: Clickstream Analysis for Sybil Detection. In USENIX Security'14.
[44]
G. Wang, M. Mohanlal, C. Wilson, X. Wang, M. Metzger, H. Zheng, and B. Y. Zhao. Social turing tests: Crowdsourcing sybil detection. In NDSS'13.
[45]
G. Wang, T. Wang, H. Zheng, and B. Y. Zhao. Man vs. machine: Practical adversarial detection of malicious crowdsourcing workers. In USENIX Security'14.
[46]
G. Wu, D. Greene, B. Smyth, and P. Cunningham. Distortion as a validation criterion in the identification of suspicious reviews. In SOMA'10.
[47]
H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao. SybilLimit: A Near-optimal Social Network Defense Against Sybil Attacks. In IEEE S&P'08.
[48]
H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman. SybilGuard: Defending Against Sybil Attacks via Social Networks. In ACM SIGCOMM'06.
[49]
Z. Zhao, J. Cheng, F. Wei, M. Zhou, W. Ng, and Y. Wu. Socialtransfer: Transferring social knowledge for cold-start crowdsourcing. In ACM CIKM'14.
[50]
C. Zhong, M. Salehi, S. Shah, M. Cobzarenco, N. Sastry, and M. Cha. Social bootstrapping: how pinterest and last. fm social communities benefit by borrowing links from facebook. In WWW'14.
[51]
E. Zhong, W. Fan, J. Wang, L. Xiao, and Y. Li. Comsoc: Adaptive transfer of user behaviors over composite social network. In ACM KDD'12.

Cited By

View all

Index Terms

  1. Strengthening Weak Identities Through Inter-Domain Trust Transfer

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    WWW '16: Proceedings of the 25th International Conference on World Wide Web
    April 2016
    1482 pages
    ISBN:9781450341431

    Sponsors

    • IW3C2: International World Wide Web Conference Committee

    In-Cooperation

    Publisher

    International World Wide Web Conferences Steering Committee

    Republic and Canton of Geneva, Switzerland

    Publication History

    Published: 11 April 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. online identities
    2. online social networks
    3. reputation
    4. security
    5. sybil attacks
    6. trust

    Qualifiers

    • Research-article

    Funding Sources

    • Space for Sharing project (S4S) http://www.space4sharingstudy.org/?page_id=78

    Conference

    WWW '16
    Sponsor:
    • IW3C2
    WWW '16: 25th International World Wide Web Conference
    April 11 - 15, 2016
    Québec, Montréal, Canada

    Acceptance Rates

    WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 01 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media