Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)

1. Privad Overview and Private Auctions Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research) Alexey Reznichenko (MPI-SWS) Saikat Guha (MSR India)

3. Can we replace current advertising systems with one that is private enough, and targets at least as well?

4. Can we replace current advertising systems with one that is private enough, and targets at least as well? • Follows today’s business model • Advertisers bid for ad space, pay for clicks • Publishers provide ad space, get paid for clicks • Deal with click fraud • Scales adequately

5. Can we replace current advertising systems with one that is private enough , and targets at least as well? • Most users don’t care about privacy • But privacy advocates do, and so do governments • Privacy advocates need to be convinced

6. Can we replace current advertising systems with one that is private enough , and targets at least as well? • Our approach: • “As private as possible” • While still satisfying other goals • Hope that this is good enough

7. Can we replace current advertising systems with one that is private enough, and targets at least as well? A principle: Increased privacy begets better targeting

8. Client Today’s advertising model (simplified) Trackers Publishers Advertisers Broker (Ad exchange)

9. Client Trackers Publishers Advertisers Broker (Ad exchange) Trackers track users Compile user profile U U U

10. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Trackers may share profiles with advertisers? U U U

11. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Client gets webpage with adbox U U U

12. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Client tells broker of page U U U

13. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Broker launches auction (for given user visiting given webpage ….) Also does clickfraud etc. U U U

14. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) (alternatively the publisher could have launched the auction) U U U

15. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Advertisers present bids and ads U U U

16. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Broker picks winners, delivers ads U U U

17. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) User waits for this exchange U U U

18. Client Trackers U U U Publishers Advertisers Broker (Ad exchange) Various reporting of results . . . . U U U

19. Broker Publishers Advertisers U Dealer SA Privad Basic Architecture Clients

20. Broker Publishers Advertisers A U Dealer Learn interest in tennis shoes SA Clients

21. Broker Publishers Advertisers A A U Anonymous request for tennis shoes Dealer SA Clients

22. Broker Publishers Advertisers A A U Dealer Relevant and non-relevant ads stored locally SA Clients

23. Chan: {Interest, Region, Language} Ad: {AdID, AdvID, Content, Targeting, . . . .} Key K unique to this request Dealer knows Client requests some channel Broker knows some Client requests this channel Dealer cannot link requests ICCCN 2010

24. Broker Publishers Advertisers A A U Dealer Webpage with adbox SA Clients

25. Broker Publishers Advertisers A A U Ad is delivered locally Minimal delay May or may not be related to page context Dealer SA Clients

26. Broker Publishers Advertisers A A U View or click is reported to Broker via Dealer Dealer SA Clients

27. Report: {AdID, PubID, EvType} Dealer learns client X clicked on some ad Broker learns some client clicked on ad Y At Broker, multiple clicks from same client appear as clicks from multiple clients ICCCN 2010

28. List of sus-pected rid’s rid: Report ID Unique for every report Used to (indirectly) inform Dealer of suspected attacking Clients Dealer remembers rid↔Client mappings Client with many reported rid’s is suspect ICCCN 2010

29. Many interesting challenges • Click fraud and auction fraud • 2nd-price, pay-per-click auction • How to do profiling • Protecting user from malicious advertisers • ….and still have good targeting • Gathering usage statistics and correlations • Accommodating multiple clients • Dynamic bidding for ad boxes • Co-existing with today’s systems

30. Many interesting challenges • Click fraud and auction fraud • 2nd-price, pay-per-click auction • How to do profiling • Protecting user from malicious advertisers • ….and still have good targeting • Gathering usage statistics and correlations • Accommodating multiple clients • Dynamic bidding for ad boxes • Co-existing with today’s systems

31. Advertising auctions today • Almost all auctions are second price • Most auctions are Pay Per Click (PPC)

32. Bid3: $6 Bid1: $2 Bid2: $7 Bid2: $3 Bid1: $5 Bid3: $1 Bid3: $4

33. Second Price Auction Winner pays bid+δ of next ranked bidder Bidders can safely bid maximum from the start Bid2: $7 Bid3: $6 Bid1: $5

34. Second Price Auction Winner pays bid+δ of next ranked bidder Bidders can safely bid maximum from the start Maximum bid Bid2: $7 ($9) Bid3: $6 ($6) Bid1: $5 ($5)

35. Second Price Auction Bidder 2 is 1st ranked Pays $6+1¢=$6.01 Bidder 3 is 2nd ranked Pays $5+1¢=$5.01 Bid2: ($9) Bid3: ($6) Bid1: ($5)

36. What about PPC (pay per click)? Click Probabilities P(C)=0.1 Bid2: $9 P(C)=0.1 Bid3: $6 P(C)=0.4 Bid1: $5

37. What about PPC (pay per click)? P(C)=0.1 $0.9 Bid2: $9 P(C)=0.1 $0.6 Bid3: $6 P(C)=0.4 $2.0 Bid1: $5 Expected Revenue Expected Revenue = Bid X Click Probability

38. What about PPC (pay per click)? P(C)=0.1 $0.9 Bid2: $9 Bid3: $6 P(C)=0.1 $0.6 P(C)=0.4 $2.0 Bid1: $5 Ad Rank= Bid X Click Probability

39. What does bidder 1 pay??? P(C)=0.1 Bid2: $9 Bid3: $6 P(C)=0.1 P(C)=0.4 Bid1: $5

40. What does bidder 1 pay??? P(C)=0.1 Bid2: $9 Bid3: $6 P(C)=0.1 P(C)=0.4 Bid1: $5 Certainly not $9+1¢=$9.01

41. Google Second Price Auction P(C)=0.4 Bid1: $5 P(C)=0.1 Bid2: $9 Bid3: $6 P(C)=0.1 Ad Rank= Bid X Click Probability P(C) next CPC = Bid next P(C) clicked

42. Google Second Price Auction P(C)=0.4 $2.26 Bid1: $5 P(C)=0.1 $6.01 Bid2: $9 Bid3: $6 P(C)=0.1 $? Ad Rank= Bid X Click Probability P(C) next CPC = Bid next P(C) clicked

43. What is the Click Probability???

44. What is the Click Probability??? • Historical click performance of the ad • Landing page quality • Relevance to the user • User click through rates • …….

45. What is the Click Probability??? • Historical click performance of the ad • Landing page quality • Relevance to the user • User click through rates • ……. Today all this is known by the broker (ad network)

46. What is the Click Probability??? • Historical click performance of the ad • Landing page quality • Relevance to the user • User click through rates • ……. In a non-tracking advertising system, the broker knows nothing about the user!

47. What is the Click Probability??? • Historical click performance of the ad • Landing page quality • ……. • Relevance to the user • User click through rates • ……. Known at broker (call it G) Known at user (call it U)

48. Second price auction with broker and user components • Ranking by revenue potential: • Assume that Click Probability = G x U • Second-Price cost per click:

49. Non-tracking advertising revisited • User profile at client • Privacy goals at broker: • Anonymity: No user identifier tied to any user profile attributes • Unlinkability: Individual user profile attributes cannot be linked

50. Finally: Problem Statement • Satisfy anonymity and unlinkability goals in a system that runs this auction: • Where Bid and G are known at broker • And U is known at client