1. Lecture 20: Privacy in Online Social Networks Xiaowei Yang

2. References: – On the Leakage of Personally Identifiable Information Via Online Social Networks by Balachander Krishnamurthy and Craig E. Wills – Characterizing Privacy in Online Social Networks by Balachander Krishnamurthy and Craig E. Wills

3. Problem Online social networks are places for users to share privacy information – Personal identifiable information (PII) Information that can be used to distinguish or trace an individual’s identity either alone or when combined with other information linkable to an individual Examples of PII – Photos – Status update However, this information can be leaked to unintended parties

4. Today Measurement studies of the importance of the problem PII can be leaked to third-party websites that make users browsing history linkable OSN default privacy settings leak PII

5. Types of private bits in OSNs

6. Who can see your private bits

7. USER PRIVACY CONTROLS Defaults are dangerous – By default, information in a user’s Facebook profile/content, and comments (as on a user’s “Wall”) are viewable by any other user in the user’s networks Has it changed? – MySpace uses similar permissive defaults in terms of access to a user’s information—all users have access to all other user’s information.

8. Do users change their defaults? A 2005 study found that – only 1.2% of college Facebook users at CMU changed the searchability of their thumbnail profile – 0.06% changed their profile visibility (second row) 75% of 200 users in the Facebook London regional network have their full profile viewable by other users in the network

9. Measurement Methodology MySpace – Generated 5000 random numeric userids in an observed range of valid userids – Retrieved their corresponding user profiles Bebo – Examined the profiles of users who were members of interest groups within Bebo

10. Facebook – Join regional networks Large and Small Geographic diversity Linguistic/culture diversity – Used the random network browsing feature of Facebook to crawl users’ profiles 10 users are displayed – 200 retrievals for each regional network 1600-1700 users

11. Results MySpace – Obtained profile information for 3851 valid userids – 79% (3046) of users retained their default settings – Profile, friends, comments and user content world viewable. Bebo – 80% of the Bebo users allowed their profile, friends, comments and user content to be viewable.

12. Facebook

14. Observations Users in smaller networks less concerned in making private information available Higher privacy value in profile information than list of friends Wall is the most valuable – 79% of those with a viewable profile allowed their Wall to be viewable to anyone in the network for NY – 83% for Seattle – 95% for the Worcester region.

15. USE OF THIRD-PARTY DOMAINS

16. Information leakage to 3 rd party domains PII is sent to 3 rd party domains via HTTP requests Same PII may be sent to the same 3 rd party domains when users browse other websites –  Online history traceable

17. HTTP Background A cookie is a piece of text stored by a web browser A cookie is sent as an HTTP header by a web server to a web browser The web browser sends it back unchanged to the server each time it accesses the server A cookie makes web browsing stateful – http is a request/response stateless protocol

18. HTTP background (cont.) An HTTP request contains – the method to be applied to the resource – Request-URI (the uniform resource identifier to the resource) – The protocol version in use Example of a Request-URI GET /pub/WWW/TheProject.html HTTP/1.1 Host: www.w3.org

19. HTTP background (cont.) Referer is a request header field Specifies to the server the address (URI) of the resource from which the Request-URI was obtained – I.e., who asked for the server URI Referer allows a server to generate customized contents

20. PII in OSNs

21. Sample of Leakage Friendid is associated with the doubleclick cookie Other sites the user browses can be linked to the friendid

22. Leakage of OSN IDs z.digg.com is a 3 rd party advertisement site

23. Leakage via External Applications

24. Leakage of pieces of PII

25. Protection Against PII Leakage User actions – Providing none in OSNs – Filtering HTTP headers Referer, Cookie – Disallow cookies – … Aggregators – Filtering PII – Are they going to do it?

26. OSNs – Strip PII from HTTP requests – A session specific value for UID External applications – Similarly, strip PII from HTTP requests

27. Problem Not Unique to OSNs Any site you have an account with can do so Examples – A news site leaks user email addresses to online aggregators – A travel site embeds a user’s first name and default airport in its cookies, and leaks them to any site hiding in its domain

28. Conclusion Eric Schmidt “If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.” By clicking the links and browsing online, they know a lot more about you than you thought

29. Discussion What can be done to improve online user privacy? – Browser isolation Next lecture: privacy-preserving online advertisements Law enforcement?

30. Lecture 21: Privacy and Online Advertising

31. References Challenges in Measuring Online Advertising Systems by Saikat Guha, Bin Cheng, and Paul Francis Serving Ads from localhost for Performance, Privacy, and Profit by Saikat Guha, Alexey Reznichenko, Kevin Tang, Hamed Haddadi, and Paul Francis

32. Problem Online advertising funds many web services – E.g., all the free stuff we get from Google Ad networks gather much user information How do they use the user information?

33. Goals Determining how well ad networks target users

34. Methodology Creating two clients representing two different user types Measuring the different ads each client sees

35. Challenges How to compare ads How to collect a representative snapshot of ads Quantifying the differences Avoiding measurement artifacts

36. Comparing Ads is challenging Ads don’t have unique IDs A & B are semantically the same, but with different text A & C are different, but with same display URLs

37. How to define two ads are the same? Easy but illegal approach: comparing destination URLs – FP: flagged as equal but not – FN: equal but not flagged Display URL has the lowest FNs  Use display URL to define ads equality

38. Taking a Snapshot More ads can be displayed on any single page How to determine all Ads that may be fed to a user? – Reload the page multiple times – But too many reloads may lead to ads churn: old ads expire, new ads show up

39. Determining the # of reloads Reloads every 5 seconds Repeated for 200 queries Curve becomes linear > 10 reloads – Ads churns Use 10 reloads as the threshold

40. Quantifying Change Metrics – Jaccard index: – Extended Jaccard index (cosine similarity)

41. Comparing Effectiveness Views: # of page reloads containing the ad Value: # of page reloads scaled by the position of the ad Overlap: Jaccard index

42. Comparing Effectiveness

43. The winner is Weight: log(views) or log(value)

44. Avoiding artifacts Different system parameters may lead to different ads view – Browsers used different DNS servers – Browsers receive different cookies – HTTP proxy

45. Analysis Configure two or more instances to differ by one parameter Comparing results for – Search Ads – Website Ads – Online Social Network Ads

46. Search Ads A, B: control w/o cookies C, D: w/ cookies enabled. Seeded w/ different personae Google 730 random product-related queries for 5 days No obvious behavioral targeting in search ads. Why? – Keyword based ads bidding Location targeting not studied

47. Websites Ads Measure 15 websites that show Google ads A, B: control in NY C: SF; D: Germany Location affects web ads

48. Website Ads A, B: control C: browse 3 out of 15 websites D and E: browse random websites and Google search random websites Google does not use browsing behavior to pick ads

49. Online social network ads Set up three or more Facebook profiles A, B: control and identical C: differs from A by one profile parameter

52. Online social network ads Use all profile parameters to customize ads Age and gender are two primary factors Diurnal patterns due to ads churn – Should it increase or decrease? Education and relationship matter less, except for engaged and non-engaged women

53. Checking Impact of Sexual Preference Six profiles with different sexual preferences Two males interested in females (male control) Two females interested in males (female control) One male interested in male One female interested in female

54. Ads differ by sexual preferences

55. Other results Found neutral ads targeted exclusively to gay men Clicking would reveal to the advertiser a user’s sexual preference 66 ads shown exclusively to gay men more than 50 times during experiments

56. Summary Search ads are largely key-word based so far Websites ads use location but probably not behavior Social network ads use all profile attributes to target users

57. Question: how can we design a privacy-preserving online advertising system?

58. Goals Support online advertising – A good revenue source to fund online services Preserve user privacy

59. PrivAd Serving Ads from a localhost client Actors: user, publisher, advertiser, broker, and dealer

60. How it works Advertisers upload ads to broker User client subscribes to a set of the ads according to the user’s profile to the broker – Message encrypted with Broker’s public key and contains a symmetric private key The Broker sends filtered ads to the user client – Ads are encrypted with the symmetric key Dealer anonymizes the client’s message to Broker

61. Ad View/Click Reporting When a user clicks an ad, the user client sends a view/click report containing ad ID and publisher ID to the broker via the dealer Dealer attaches a unique report ID, removes client identity information, maps the ID to the user identity information

62. Click-fraud defense Broker provides dealer the record IDs if it suspects click-fraud The dealer finds the user The dealer stops relaying ads to user if convinced Questions not answered: how to detect by broker, and what’s the punishment

63. Defining User Privacy Unlinkability – No single player can link the identity of user with any piece of user’s profile – No single player can link together more than some limited number of pieces of personalization information of a given user The dealer learns User A clicks on some ad The broker learns someone clicked on ad X Not robust to dealer/broker collusion

64. Scaling PrivAd Ads churn is significant 2GB/month of compressed ad data

65. Discussion What challenges does PrivAd may face in a practical deployment?