Assessing the Veracity of Identity Assertions via OSNs

1. Assessing the Veracity of Identity Assertions via OSNs Michael Sirivianos Telefonica Research with: Kyunbaek Kim (UC Irvine), Jian W. Gan (TellApart), Xiaowei Yang (Duke University)

2. Leveraging Social Trust to addressa tough problem • Assessing the credibility of identity statements • made by web users

3. Why Social Trust? • It requires effort to built up social relationships: • The social graph can be used to defeat Sybils • Online Social Networks (OSN) help users to • organize and manage their social contacts • Easy to augment the OSN UI, with features • that allow users to declare who they trust and • and by how much

4. She wants me to prove I am not a dog  An online world without identity credentials makes determining who and what to believe difficult

6. How can ``Merkin’’ convince us that he is a chef?

7. More Motivating Scenarios • We need a new online identity verification • primitive that: • enables users to post assertions about their • identity • informs online users and services on whether • they should trust a user’s assertions • preserves the anonymity of the user that • posts the assertion • does not require strong identity or infrastructure • changes • Trustworthy online communication: • Dating websites, Craigslist, eBay transactions • first contact in OSNs • ``I work in ...”, ``I am an honest seller”, • ``My name is ” • Access control • Age-restricted sites: ``I am over 18 years old’’

8. Our Approach • Crowd-vetting • Employ friend feedback (tags) to determine • whether an online user’s assertion is credible • A new application of OSNs and user-feedback • OSNs have so far been used to block spam • (Re:, Ostra) and for Sybil-resilient online • voting (Sumup) • User-feedback has so far been used • for recommendations (Yelp, Amazon, • YouTube, eBay etc) Our main contribution lies in combining OSNs and user feedback to provide credible assertions

9. Our Solution: FaceTrust • Online social network users tag their friends’ • identity assertions • OSN providers issue web-based credentials on a • user’s assertions using his friends’ feedback • bind the assertion to a measure of its credibility • for not very critical applications, but they • can help users or services make informed decisions • Uses trust inference to prevent manipulation

10. Social Tagging • A Facebook app ``with a purpose’’ • Users post assertions on their OSN profiles: • e.g., “Am I really over 18 years old?” • Friends tag those assertions as TRUE or FALSE

13. An Amazon review example • I want to write a scathing review for Sergei’s book • I want to prove that I am indeed a • CS researcher, thus my review is • authoritative and readers can take it seriously • I don’t want Sergei to know I wrote the review • Amazon’s ``Real Name’’ is not an option

16. We aim at determining the ground truth on • identity assertions • We assume that user beliefs reflect the ground • truth

17. Because user feedback is not fully reliable, we • provide a credibility measure in 0-100% that • should correlate strongly with the truth • We refer to this measure as Assertion Veracity • Verifiers can use thresholds suggested by • the OSN provider, e.g., accept as true if > 50%

18. Outline • How to defend against colluders and Sybils? • Manipulation-resistant assertion veracity • Trust inference • OSN-issued web-based credentials • Unforgeable credentials • Anonymous and unlinkable credentials • Evaluation

19. Our Non-manipulability Objective • It should be difficult for dishonest users to post • assertions that appear true • Ensure that honest users can post assertions • thatappear true

20. Veracity values should be informative • The veracity values should correlate strongly • with the truth. • If an assertion has higher veracity than another • → the assertion is more likely to be true • Useful for devices/users that have prior • experience with the system and know the • veracity values of known true assertions

21. Manipulation-resistant Assertion Veracity • User j posts an assertion. Only his friendi can • tag the assertion • Use weighted average of tags dijby friends ion • j’s assertion • If TRUE, dij = +1 . If FALSE dij = -1 • j’s assertion veracity =max(i wi dij/ i wi , 0) • To defend against colluders that have low weight: • if i wi < M, assertion veracity = 0 FALSE tags matter more Tags weighted by wi

22. Tagger Trustworthiness • Each tagger iis assigned a tagger trustworthiness score • assertion veracity =max(i wi dij/ i wi , 0) • Trust inference analyzes the social graph of taggers • and their tagging history Tagger credibility derived via Trust Inference

23. Trust Inference via the Social Graph • Honest users tend to befriend honest users • each edge in the social graph implies trust • Annotate trust edges by tagging similarity: • History-defined similarity = • #same-tags / #common-tags • e.g., if 2 friends have tagged the same 2 assertions • of a common friend and agree on only 1 tag, • they have 50% similarity • linearly combine history- and user-defined • similarity (``Do I honestly tag my friends?”) Difficult for Sybils to establish similarity-annotated trust edge with honest users

24. Our Trust Inference Problem • Dishonest users may employ Sybils • Dishonest users can try to build up high • similarity with their honest friends • The input is the trust graph G(V, E) • V are the users in the social network • E are directed friend connections annotated by • tagging similarity • The output is the tagger trustworthiness of users

25. Similarity-annotated Trust Graph Tagging similarity Dishonest node Trust Seed s1 Honest region Dishonest region Bottleneck 50% 75% 100% u2 u3 u4 100% 80% . . Attack edge 100% . Honest node u2 Sybil node Sybil nodes • We need a Sybil-resilient trust inference method • the trust dishonest users and Sybils can obtain • should be limited by edges connecting them to • honest region • How to translate this trust graph into tagger • trustworthiness values for all users? • Plethora of prior work on trust inference • Having multiple trust seeds reduces the trust • a dishonest user can obtain by focusing on a seed • The closer and the better connected the dishonest • user is to the trust seed, the greater the trust it • can obtain • The trust inference method determines how trust • propagates from trusted seed to users

26. MaxTrust • We transform the similarity-annotated trust graph • into a single-source/single-sink flow graph • The cost of computation should not increase • with the number of trusted seeds • Unlike Advogato, cost of MaxTrust’s max-flow • heuristic is independent of the number of seeds • O(Tmax |E| log |V|) • The tagger trustworthiness of a user is • 0 ≤ wi≤ Tmax in increments of 1

27. Outline • How to defend against colluders and Sybils? • Manipulation-resistant assertion veracity • Sybil-resilient trust inference • OSN-issued web-based credentials • Unforgeable credentials • Anonymous and unlinkable credentials • Evaluation • Introducing social trust to collaborative spam mitigation

28. OSN-issued credentials • Issued by the OSN provider: • {assertion type, assertion, assertionveracity} • Simple and web-based • Easy to parse by human users with no need to • understand cryptographic tools • [``Why Johnny can’t Encrypt’’, Security 99] • XML web API to enable online services to read • credentials

30. Unforgeable credentials • the user binds the credential to the context • he is issuing it for. Thus, no user can reuse it in • another context

33. Anonymous credentials • as long as OSN provider is trusted and the • assertion does not contain personally identifiable • info • Unlinkable credentials • as long as the user does not list it along with • his other credentials and he creates a new • credential for each distinct verifier

34. Effectiveness Simulation • Evaluating the effectiveness of trust inference in • our setting: • user feedback weighted by trust derived from • the social graph and tagging history • How well do credibility scores correlate with the truth? • Can the design withstand dishonest user tagging and • Sybil attacks?

35. Veracity is reliable under Sybils • 50% of users is honest. Veracity of true assertions • is substantially higher than veracity of dishonest • The number of Sybils does not affect it

36. Facebook Deployment • Do users tag? • Is the UI sufficiently intuitive and attractive? • Do users tag honestly? • Does trust inference work in real life? • Measure average veracity of a’ priori known • true and false assertions • Data set • 395-user Facebook AIR social graph • 14575 tags, 5016 assertions, 2410 social connections

37. Users tag honestly • Veracity correlates strongly with the truth • Real users tag mostly honestly

38. FaceTrust contributions • A solution to the problem of identity • verification in non-critical online settings • crowd-vetting through social tagging • trust inference for attack resistance • simple, lightweight web-based credentials with • optional anonymity and unlinkability. • Deployment of our social tagging and • credential issuing front-ends • collection of real user tagging data • proof of feasibility

39. Thank You! Facebook application “Am I Really?” at: http://apps.facebook.com/am-i-really Questions?

40. The bar for the non-manipulability of user-feedback-based systems is low • Plain majority voting • Weighted majority voting • reduces the weight of votes submitted by • friends, but easily manipulable with Sybils Yet users still rely on them to make informed decisions

41. Threat Model • Dishonest users • tag as TRUE the dishonest assertions posted by • colluding dishonest users • create Sybils that tag their dishonest assertions • as TRUE • tag as TRUE the honest assertions posted • by honest users to build trust with honest users • Sybils, which are friends only with dishonest users, • post assertions, which cannot be voted FALSE • (Sybil assertion poster attack)

42. Assumptions • Honest users • tag correctly to the best of their knowledge • when they tag the same assertions, their tags • mostly match • most of their friends will not tag their • honest assertions as FALSE • do not indiscriminately add friends