Third-party Identity Management Usage on the Web
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1 Third-party Identity Management Usage on the Web Anna Vapen¹, Niklas Carlsson¹, Anirban Mahanti², Nahid Shahmehri¹ ¹Linköping University, Sweden ²NICTA, Australia
2 Third-party Web Authentication Web Authentication Registration with each website Many passwords to remember Third-party authentication Use an existing IDP (identity provider) account to access an RP (relying party) Log in less often; Stronger authentication Increase personalization opportunities Share information between websites 2
3 Motivation An emerging third-party authentication landscape Increasing usage of third-party identity providers Complex, nested relationships between RPs and IDPs Authorization protocol (OAuth) used for authentication Applications acting on user s behalf Data transfer between parties; Less control over data IDP selection Privacy implications 3
4 Contributions Novel Selenium-based data collection methodology Identification and validation of RP-IDP relationships Popularity-based logarithmic sampling technique Characterization of identified RP-IDP relationships Impact on IDP selection of RP characteristics Comparison to third-party content-delivery relationships 4
5 Methodology (1) Popularity-based logarithmic sampling 80,000 points uniformly on a logarithmic range Power-law distribution Capturing data from different popularity segments 1 million most popular websites Sampled websites 5
6 Methodology (2) Selenium-based crawling and relationship identification Able to process Web 2.0 sites with interactive elements Low number of false positives Validation with semi-manual classification and text-matching 1 million most popular websites Sampled websites 6
7 Collected Data 1,6 terabyte analyzed data 25 million analyzed links WHOIS, server location and audience location 35,620 sampled sites 3,329 unique relationships 50 IDPs and 1,865 RPs Total site size and number of links and objects 7
8 IDP Usage More than 75% of the RPs are served by 5% of the IDPs RPs tend to select popular sites as IDPs Only 15 of the 44 IDPs outside top 10 on Alexa serve more than 10 sampled RPs 75% of RPs served by 5% of IDPs 8
9 Top IDPs IDP rank Alexa rank IDP Protocol Number of IDP relationships 1 2 Facebook.com Oauth Twitter.com OAuth QQ.com OAuth Google.com Oauth / OpenID Yahoo.com Oauth / OpenID 141 ** 6 16 Sina.com.cn Oauth OpenID field OpenID 87 * ** Vkontakte.ru Oauth Weibo.com Oauth Linkedin.com Oauth 63 * Domain change to vk.com ** Authentication with Sina.com.cn redirects to Weibo.com Login with any OpenID provider 9
10 Top IDPs IDP rank Alexa rank IDP Protocol Number of IDP relationships 1 2 Facebook.com Oauth Twitter.com OAuth QQ.com OAuth Google.com Oauth / OpenID Yahoo.com Oauth / OpenID 141 ** 6 16 Sina.com.cn Oauth OpenID field OpenID 87 * ** Social networks (except no. 7) Vkontakte.ru Oauth Weibo.com Oauth Linkedin.com Oauth 63 * Domain change to vk.com ** Authentication with Sina.com.cn redirects to Weibo.com Login with any OpenID provider 10
11 IDP Selection Popular sites as IDPs, instead of specialized IDPs Popular sites with Lots of existing users Personal information Specialized IDPs with stronger authentication methods 11
12 Number of IDPs per sampled RP IDP widgets providing a pre-selected large set of IDPs Estimated weighted average: < 3 IDPs / RP 12
13 Number of IDPs per RP IDPs per RP Based on Popularity [1-10] (10-10^2] (10^2-10^3] (10^3-10^4] Alexa site rank of RPs (10^4-10^5] (10^5-10^6]3 > 10^6 (10^5-10^6]3 (10^4-10^5] (10^3-10^4] (10^2-10^3] (10-10^2] [1-10] Breakdown of the average number of IDPs selected per RP and popularity segment 13
14 Comparison with Content Services Content: scripts, images and other third-party objects IDPs much more popular sites than content providers 14
15 Service-based Analysis 200 most popular websites Manual classification Likely to be RPs Using social IDPs: file sharing, info Likely to be IDPs; Many RPs in this category News File sharing Info Social/portal Video Tech Commerce Ads CDN Early adopters, using several IDPs Using IDPs from same category: tech, commerce 15
16 Cultural and Geographical Analysis North American and Chinese RPs use local IDPs to a large extent Content delivery usage less biased to local providers North America Europe China Russia Asia (all) Identity management Content delivery North America Europe China Russia Asia (rest) Other 16
17 Summary and Conclusions Large-scale characterization of third-party Web authentication Novel data collection methodology with popularity-based sampling Few large third-parties serve many websites Comparison with content sharing IDP selection much more biased Risk for privacy leaks Few large third-parties handling a lot of information The most popular IDPs are using protocols not adapted for strong authentication 17
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