Lecture 21: Privacy and Online Advertising

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

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?

Goals Determining how well ad networks target users

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

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

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

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

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

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

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

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

Comparing Effectiveness

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

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

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

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

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

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

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

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

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

Ads differ by sexual preferences

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

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

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

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

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

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

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

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

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

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

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