Web Authentication using Third-parties in Untrusted Environments
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1 Web Authentication using Third-parties in Untrusted Environments Anna Vapen PhD Thesis Presentation Supervisors: Nahid Shahmehri, Niklas Carlsson
2 *****
3 3 Agenda 1. Background 2. Research problems 3. Analysis Web authentication and untrusted computers The third-party authentication landscape Third-parties and privacy risks 4. Contributions
4 4 Background ****
5 Background 5 Web Authentication Method to prove that you are a specific person Personal web experience User accounts require authentication Example: Signing in to Google with username and password
6 Background 6 Password Challenges Most common web authentication method Simple setup Reused on several sites Written down Replay attacks Forgotten by the user Alternative methods Time consuming Additional equipment
7 Background 7 Mobile Users and Untrusted Environments Mobile users Different devices Different places Untrusted environments Infected computer Untrusted WiFi network
8 Background 8 Third-party Web Authentication Use an IDP (identity provider) account to access many RPs (relying parties) Fewer logins simplify authentication Information sharing between websites Privacy leaks!
9 Background 9 Third-party Authentication Scenario Identity provider (IDP) Redirect Logged in Relying party (RP)
10 Research Problems 10
11 Research problems 11 Research Problems 1. Web authentication For mobile users in untrusted environments? 2. Third-party authentication Usage over time? How to measure? 3. Privacy risks Information flows between parties?
12 Web Authentication and Untrusted Computers 12
13 Web authentication 13 Mobile Phones as Authentication Devices Strong authentication Security problems Carried by the user Comparing solutions?
14 Web authentication Design and Evaluation Method Design Select requirements Get design suggestions Requirements Security Availability Login Evaluation Start with an existing design Get a security rating of the design Login Security rating PrimeLife 11
15 Web authentication Optical Authentication Proof-of-Concept (3) Response generated (2) Take a picture of the challenge (1) Challenge barcode shown on screen Logged in! (4) Show response to webcam IJMCMC 11
16 The Third-party Authentication Landscape 16
17 3 rd -party authentication 17 Data Collection Popularity-based logarithmic sampling 80,000 points uniformly on a logarithmic range Pareto-like distribution Capturing data from different popularity segments 1 million most popular websites Sampled websites PAM 14
18 3 rd -party authentication 18 Large-scale Crawling 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 textmatching 1 mil Sampled websites Crawl sites to depth 2 PAM 14
19 3 rd -party authentication 19 Collected Data 1.6 terabyte analyzed data 25 million analyzed links WHOIS, server location, and audience location unique relationships 50 IDPs and RPs Total site size and number of links and objects PAM 14, IC 16
20 3 rd -party authentication 20 IDPs vs Content Sharing Services Content sharing: Importing images, scripts etc. from other sites (third-party content providers) IDPs are selected locally, in contrast to content services. PAM 14
21 3 rd -party authentication 21 Service-based Analysis of RPs Commerce Likely to be IDPs Early adopters, using several IDPs Tech Video Social/portal Manual analysis: Top 200 websites in April 2012 File sharing Info News Using social/portal IDPs Ad services, CDNs PAM 14
22 Third-parties and Privacy Risks 22
23 Privacy risks 23 App Rights and Information Flows Read RP IDP Actions: Write Update/remove App rights example SEC 15, UEOP 16
24 Privacy risks 24 Our Studies on Privacy Risks Categorization app-rights data Manual study on the top 200 most popular websites Longitudinal approach: three years Targeted login tests Privacy risk categorization Data types in app rights Combinations of types
25 Privacy risks 25 Protocol Selection OpenID Authentication protocol Decreasing in popularity OAuth RP may use actions on IDP Rich user data is shared Increasingly popular -11% +24% April 2012 vs. Sept 2014 OAuth OpenID Both SEC 15, UEOP 16
26 Privacy risks 26 IDP Selection Top 200 April 2012: 69 RPs and 180 relationships Same sites, April 2015: +15 RPs and +33 relationships 75% of these RPs are selecting all their IDPs from the top 5 most popular IDPs + 37% Top IDPs: + 19% + 12% SEC 15, UEOP 16
27 Privacy risks 27 Risk Types Facebook, Twitter and Google: Only a few relationships in the most privacy preserving category 2+ IDPs: More than half are using actions Dangerous when having several IDPs Potential multi-hop leakage 2+ IDPs 51% actions SEC 15
28 Privacy risks 28 Multi-account Information Risks Private photos IDP 1 RP IDP 2 This is me! Cross account leakage Unwanted combinations of conflicting information RPs handle multi-idp usage badly Connecting several IDPs to an RP SEC 15
29 Privacy risks 29 Structures in the RP-IDP Landscape IDP IDP 1 IDP 2 IDP Hybrid: RP and IDP HY RP 1 RP 2 High-degree IDP case IDP having many RPs Top IDPs RP High-degree RP case RP having many IDPs Specialized IDPs RP Hybrid case Hybrids are both RP and IDP UEOP 16
30 Privacy risks 30 RP-to-RP Leakage Example IDP RP 1 RP 2 RP-to-RP leaks February 2014 April 2015 IDP All Severe All Severe Facebook Twitter Google RP-to-RP Potential RP-to-RP leaks Data posted to IDP from RP1 Data read from IDP to RP2 Dataset with 44 RPs using Facebook, 14 using Twitter and 12 using Google UEOP 16
31 Contributions 31
32 Contributions 32 Contributions Design and evaluation method Large-scale RP-IDP measurements Novel measurement method Categorization of RP-IDP relationships Privacy risks and information sharing Protocol analysis Structural properties
33 Web Authentication using Third-parties in Untrusted Environments Anna Vapen Papers included in this thesis: Security Levels for Web Authentication using Mobile Phones, PrimeLife'11 2-clickAuth - Optical Challenge-Response Authentication using Mobile Handsets, IJMCMC'11 Third-party Identity Management Usage on the Web, PAM'14 A Look at the Third-Party Identity Management Landscape, IC'16 Information Sharing and User Privacy in the Third-party Identity Management Landscape, SEC'15 Longitudinal Analysis of the Third-party Authentication Landscape, UEOP'16
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