Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)
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Transcript of Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)
![Page 1: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/1.jpg)
Paul Francis (MPI-SWS)Ruichuan Chen (MPI-SWS)Bin Cheng (NEC Research)Alexey Reznichenko (MPI-SWS)Saikat Guha (MSR India)
Privad Overview and Private Auctions
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![Page 3: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/3.jpg)
Can we replace current advertising systems with one that is private enough, and targets at least as well?
![Page 4: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/4.jpg)
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
• 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
![Page 5: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/5.jpg)
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
• Most users don’t care about privacy• But privacy advocates do, and so do governments• Privacy advocates need to be convinced
![Page 6: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/6.jpg)
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
Our approach:• “As private as possible”• While still satisfying other goals• Hope that this is good enough
![Page 7: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/7.jpg)
Can we replace current advertising systems with one that is private enough, and targets at least as well?
A principle: Increased privacy begets better targetingA principle: Increased privacy begets better targeting
![Page 8: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/8.jpg)
Today’s advertising model(simplified)
Client
AdvertisersPublishers
Trackers
Broker (Ad exchange)
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Broker (Ad exchange)
Client
AdvertisersPublishers UU
Trackers
U
Trackers track usersCompile user profileTrackers track usersCompile user profile
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Broker (Ad exchange)
U
Client
Publishers UU
Trackers
U
U
Trackers may share profiles with advertisers?
Trackers may share profiles with advertisers?
Advertisers
U
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Client gets webpage withadbox
Broker (Ad exchange)
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Client tells broker of page
Broker (Ad exchange)
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Broker launches auction (for given user visiting given webpage ….)Also does clickfraud etc.
Broker launches auction (for given user visiting given webpage ….)Also does clickfraud etc.
Broker (Ad exchange)
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
(alternatively the publisher could have launched the auction)
(alternatively the publisher could have launched the auction)
Broker (Ad exchange)
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Advertisers present bids and ads
Advertisers present bids and ads
Broker (Ad exchange)
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Broker (Ad exchange)
U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Broker picks winners, delivers ads
Broker picks winners, delivers ads
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Broker (Ad exchange)
User waits for this exchangeUser waits for this exchange
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U
Client
Publishers UU
Trackers
U
U
Advertisers
U
Various reporting of results . . . .
Various reporting of results . . . .
Broker (Ad exchange)
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Broker
Publishers
Clients
Advertisers
SA
U
Dealer
Privad Basic Architecture
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Broker
Publishers
Clients
Advertisers
SA
U
A
Dealer
Learn interest in tennis shoes
Learn interest in tennis shoes
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Broker
Publishers
Clients
Advertisers
SA
U
A
A
Anonymous request for tennis shoes
Dealer
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Broker
Publishers
Clients
Advertisers
SA
U
A
A
Relevant and non-relevant ads stored locally
Dealer
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ICCCN 2010 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
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Broker
Publishers
Clients
Advertisers
SA
U
Dealer
A
A
Webpage withadbox
![Page 25: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/25.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
A
A
Ad is delivered locally
Minimal delay
May or may not be related to page context
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Broker
Publishers
Clients
Advertisers
SA
U
A
A
View or click is reported to Broker via Dealer
Dealer
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ICCCN 2010 27
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
Report: {AdID, PubID, EvType}
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ICCCN 2010 28
rid: Report IDUnique for every report
List of sus-pected rid’s
Used to (indirectly) inform Dealer of suspected attacking Clients
Dealer remembers rid↔Client mappings
Client with many reported rid’s is suspect
![Page 29: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/29.jpg)
Many interesting challenges
Click fraud and auction fraud2nd-price, pay-per-click auctionHow to do profilingProtecting user from malicious advertisers
….and still have good targetingGathering usage statistics and correlationsAccommodating multiple clients
Dynamic bidding for ad boxesCo-existing with today’s systems
![Page 30: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/30.jpg)
Many interesting challenges
Click fraud and auction fraud2nd-price, pay-per-click auctionHow to do profilingProtecting user from malicious advertisers
….and still have good targetingGathering usage statistics and correlationsAccommodating multiple clients
Dynamic bidding for ad boxesCo-existing with today’s systems
![Page 31: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/31.jpg)
Advertising auctions today
• Almost all auctions are second price
• Most auctions are Pay Per Click (PPC)
![Page 32: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/32.jpg)
Bid1: $2
Bid2: $3
Bid3: $1Bid3: $4
Bid1: $5
Bid3: $6
Bid2: $7
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Bid1: $5
Bid3: $6
Bid2: $7
Second Price Auction
• Winner pays bid+δ of next ranked bidder
• Bidders can safely bid maximum from the start
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Bid1: $5 ($5)
Bid3: $6 ($6)
Bid2: $7 ($9)
Second Price Auction
• Winner pays bid+δ of next ranked bidder
• Bidders can safely bid maximum from the start
Maximum bid
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Bid1: ($5)
Bid3: ($6)
Bid2: ($9)
Second Price Auction
• Bidder 2 is 1st ranked– Pays $6+1¢=$6.01
• Bidder 3 is 2nd ranked– Pays $5+1¢=$5.01
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Bid1: $5
Bid3: $6
Bid2: $9
What about PPC (pay per click)?
P(C)=0.4
P(C)=0.1
P(C)=0.1 ClickProbabilities
![Page 37: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/37.jpg)
Bid1: $5
Bid3: $6
Bid2: $9
What about PPC (pay per click)?
P(C)=0.4 $2.0
P(C)=0.1 $0.6
P(C)=0.1 $0.9
ExpectedRevenue
Expected Revenue = Bid X Click Probability
![Page 38: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/38.jpg)
What about PPC (pay per click)?
Bid1: $5 P(C)=0.4 $2.0
Ad Rank= Bid X Click Probability
Bid3: $6
Bid2: $9
P(C)=0.1 $0.6
P(C)=0.1 $0.9
![Page 39: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/39.jpg)
What does bidder 1 pay???
Bid1: $5 P(C)=0.4
Bid3: $6
Bid2: $9
P(C)=0.1
P(C)=0.1
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What does bidder 1 pay???
Bid1: $5 P(C)=0.4
Certainly not $9+1¢=$9.01
Bid3: $6
Bid2: $9
P(C)=0.1
P(C)=0.1
![Page 41: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/41.jpg)
Google Second Price Auction
Bid1: $5 P(C)=0.4
CPC = Bid next
Bid3: $6
Bid2: $9
P(C)=0.1
P(C)=0.1
Ad Rank= Bid X Click Probability
P(C) nextP(C) clicked
![Page 42: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/42.jpg)
Google Second Price Auction
Bid1: $5 P(C)=0.4 $2.26
CPC = Bid next
Bid3: $6
Bid2: $9
P(C)=0.1 $?
P(C)=0.1 $6.01
Ad Rank= Bid X Click Probability
P(C) nextP(C) clicked
![Page 43: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/43.jpg)
What is the Click Probability???
![Page 44: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/44.jpg)
What is the Click Probability???
• Historical click performance of the ad
• Landing page quality• Relevance to the user• User click through
rates• …….
![Page 45: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/45.jpg)
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)
![Page 46: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/46.jpg)
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!
![Page 47: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/47.jpg)
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)
![Page 48: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/48.jpg)
Second price auction with broker and user components
• Ranking by revenue potential: – Assume that Click Probability = G x U
• Second-Price cost per click:
![Page 49: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/49.jpg)
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
![Page 50: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/50.jpg)
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
![Page 51: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/51.jpg)
Basic Architecture
![Page 52: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/52.jpg)
Two questions
• Where do we do the ranking?
• Where do we do the CPC computation?
![Page 53: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/53.jpg)
Two questions
• Where do we do the ranking?
• Where do we do the CPC computation?
Do CPC at Broker: •Don’t want to reveal advertiser’s Bid•Fraud
![Page 54: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/54.jpg)
Bid, G
U
party
3U Bid, G
Rank@Client
Rank@Broker
Rank@3rdParty
Three flavors of Non-Tracking auctions
Broker(Bid, G)
Client(U)
![Page 55: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/55.jpg)
Bid, G
U
party
3U Bid, G
Rank@Client
Rank@Broker
Rank@3rdParty
Three flavors of Non-Tracking auctions
Broker(Bid, G)
Client(U)
![Page 56: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/56.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
![Page 57: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/57.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
![Page 58: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/58.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
Ac - clicked ad ID((Bn × Gn) × Un / Uc)
E[Bc, Gc] Decrypts E[Bc, Gc]Computes CPC: ((Bn × Gn) × Un / Uc) / Gc
Checks that CPC ≤ Bc
Time
![Page 59: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/59.jpg)
Broker(Bid, G)
Client(U)
Decrypts E[Bc, Gc]Computes CPC: ((Bn × Gn) × Un / Uc) / Gc
Checks that CPC ≤ Bc
![Page 60: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/60.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
User information obscured by
hiding within this composite value
Ac - clicked ad ID((Bn × Gn) × Un / Uc)
E[Bc, Gc] Decrypts E[Bc, Gc]Computes CPC: ((Bn × Gn) × Un / Uc) / Gc
Checks that CPC ≤ Bc
![Page 61: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/61.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
Ac - clicked ad ID((Bn × Gn) × Un / Uc)
E[Bc, Gc] Decrypts E[Bc, Gc]Computes CPC: ((Bn × Gn) × Un / Uc) / Gc
Checks that CPC ≤ Bc
![Page 62: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/62.jpg)
A - the ad ID,Value of (B × G),E[B,G],(+ targeting etc.)
Computes ranking: (B × G) × U
Broker(Bid, G)
Client(U)
Ac - clicked ad ID((Bn × Gn) × Un / Uc)
E[Bc, Gc] Decrypts E[Bc, Gc]Computes CPC: ((Bn × Gn) × Un / Uc) / Gc
Checks that CPC ≤ Bc
Bc and Gc may have changed
between ranking and CPC
calculation
![Page 63: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/63.jpg)
All three auction designs introduce various system delays
• precompute and cache ranking• use out-of-date bid information• do not immediately reflect changes
in bids
![Page 64: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/64.jpg)
Changes in bids constitute main source of churn
Advertisers constantly update their bids to•show ads in a preferred position•meet target number of impressions•respond to market changes
![Page 65: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/65.jpg)
How detrimental are auction delays?
Broker perspective:•How much revenue is lost due to these delays?
Advertiser perspective:•How they affect advertisers’ rankings?
![Page 66: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/66.jpg)
Bing’s Auction log
• 2TB of log data spanning 48 hours• 150M auctions with 18M ads• Trace record for an auction includes:
– All participating ads– Bids and quality scores – Whether ad was shown and clicked
![Page 67: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/67.jpg)
Understanding effect of churn on revenue
Idea:•Simulate auctions with stale bid information•Compute auctions at time t using bids recorded at time t-x•Compare generated revenue to auctions with up-to-date bid information
![Page 68: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/68.jpg)
We cannot predict changes in clicking behavior when rankings change
![Page 69: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/69.jpg)
We simulate five click models
1. 100% same position2. 75% same position, 25% same ad3. 50%-50%4. 25% same position, 75% same ad5. 100% same ad
![Page 70: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/70.jpg)
Bid staleness and change in revenue
![Page 71: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/71.jpg)
Bid staleness and change in ranking
![Page 72: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/72.jpg)
Computing U
So far, we assume we know user component of click probability
Hard to compute purely at client
Not enough history
Unlinkably gather click stats from clients, compute U, feed back to clients
![Page 73: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/73.jpg)
Assume a set of factors X={x1, x2, …, xL}
Clients report {Ad-ID, X, click}
Level of interest in ad’s product/service
Targeting/user match quality
Webpage context
User’s historic CTR…….
Broker computes U = f(X), delivers f(X) along with ad
![Page 74: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/74.jpg)
Problem if X={x1, x2, …, xL} fingerprints user
Possible mitigating factors:
Level of interest
Targeting match quality
Webpage context
User’s historic CTR
Many interests change, many interests don’t correlate that well
Different ads have different targeting
Can be course-grained
Can be course-grained
![Page 75: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/75.jpg)
Future work
So far, designs appear practical, but:•Can we accurately compute user score U?
– And without violating privacy….
•Are there new forms of click fraud?
•Need experience in practice….
![Page 76: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/76.jpg)
User Statistics
What kind of targeting works best?
Broker and advertiser want to know deep statistical information about users
Centralized systems have full knowledge
When should ads be shown?Are users interested in A also interested in B?How can conversion rates be improved?
How can Privad privately provide this information?
![Page 77: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/77.jpg)
Differential Privacy
Differential Privacy adds noise to answers of DB queries
Normally modeled as a single trusted DB
Such that presence or absence of single DB element cannot be determined
DB
Query
True Answer
Add Noise
Noisy Answer
Trusted
![Page 78: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/78.jpg)
Distributed Differential Privacy
DB
Query
(cleartext)
DealerDealerDB
DB
DB
![Page 79: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/79.jpg)
Distributed Differential Privacy
DB
True Answers(encrypted)
DealerDealerDB
DB
DB
Noisy Answers(encrypted)
![Page 80: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/80.jpg)
A couple URLs
adresearch.mpi-sws.org
trackingfree.org
![Page 81: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/81.jpg)
• Backups, trust model
ICCCN 2010 81
![Page 82: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/82.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
Generate user profiles locally at the client
In other words, Adware!
Generate user profiles locally at the client
In other words, Adware!
Software AgentSoftware Agent
![Page 83: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/83.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
Anonymizes client-broker communications
Cannot eavesdrop
Helps with clickfraud
Anonymizes client-broker communications
Cannot eavesdrop
Helps with clickfraud
![Page 84: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/84.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Client/broker messages:
Contain minimal info (no PII)
Cannot be linked to same client
Client/broker messages:
Contain minimal info (no PII)
Cannot be linked to same client
Dealer
![Page 85: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/85.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
Unlinkability and anonymity
![Page 86: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/86.jpg)
Broker
Publishers Advertisers
Dealer
UntrustedBlack-box
U
SoftwareAgent
Reference Monitor
Trusted, open
Cleartext
Encrypted
Clients
SA
U
Browser sandbox
![Page 87: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/87.jpg)
Broker
Publishers Advertisers
Dealer
Clients
SA
U
Honest but temptedHonest but tempted
“Pretty honest but very curious”
Doesn’t collude
Possibly malicious
UntrustedBlack-box
Reference Monitor
Trusted, open
Cleartext
Encrypted
Browser sandbox
U
SoftwareAgent
![Page 88: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/88.jpg)
Honest but curious isn’t quite right
Privacy and threat models???
No formal privacy modelFormal models are too narrow and restrictive
We expect the broker to do “what it can get away with”, but cautiously
Plus we need to make privacy advocates comfortable
![Page 89: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/89.jpg)
Clients
SA
U
DealerDealer and Software Agent are new components
How are they incentivized?
![Page 90: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/90.jpg)
Clients
SA
U
Dealer Dealer:
Legally bound to follow protocols, not collude
Execute open-source software, open to inspection
![Page 91: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/91.jpg)
Clients
SA
U
Dealer Client:
Various options:
Provide benefit: free software, content, …..
Like adware!
Bundle with browser or OS
![Page 92: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/92.jpg)
• Local threats…
ICCCN 2010 92
![Page 93: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/93.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer Please suspend disbelief, imagine that we succeed……
![Page 94: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/94.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
A
Perfectly Private Advertising System
![Page 95: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/95.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
A
Perfectly Private Advertising System
Ad
Ad targeted to:“Man” AND “Married” AND“Has girlfriend”Ad
![Page 96: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/96.jpg)
Broker
Publishers
Clients
Advertisers
SA
U
Dealer
A
Perfectly Private Advertising System
Click
Advertiser gets (very) personal information about users
![Page 97: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/97.jpg)
ICCCN 2010 97
Honey, why are you getting ads
for sexy lingerie?
![Page 98: Paul Francis (MPI-SWS) Ruichuan Chen (MPI-SWS) Bin Cheng (NEC Research)](https://reader035.fdocuments.in/reader035/viewer/2022081516/5681501b550346895dbe0136/html5/thumbnails/98.jpg)
Privacy
Targeting
More
Less
Worse Better
Privad
???