lecture 21: privacy and online advertising
DESCRIPTION
Lecture 21: Privacy and Online Advertising. References. Challenges in Measuring Online Advertising Systems by Saikat Guha , Bin Cheng, and Paul Francis - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/1.jpg)
Lecture 21: Privacy and Online Advertising
![Page 2: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/2.jpg)
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
![Page 3: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/3.jpg)
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?
![Page 4: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/4.jpg)
Goals
• Determining how well ad networks target users
![Page 5: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/5.jpg)
Methodology
• Creating two clients representing two different user types
• Measuring the different ads each client sees
![Page 6: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/6.jpg)
Challenges
• How to compare ads
• How to collect a representative snapshot of ads
• Quantifying the differences
• Avoiding measurement artifacts
![Page 7: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/7.jpg)
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
![Page 8: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/8.jpg)
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
![Page 9: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/9.jpg)
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
![Page 10: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/10.jpg)
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
![Page 11: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/11.jpg)
Quantifying Change• Metrics– Jaccard index:
– Extended Jaccard index (cosine similarity)
||||
BABA
![Page 12: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/12.jpg)
Comparing Effectiveness
• Views: # of page reloads containing the ad• Value: # of page reloads scaled by the position of
the ad• Overlap: Jaccard index
![Page 13: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/13.jpg)
Comparing Effectiveness
![Page 14: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/14.jpg)
The winner is
• Weight: log(views) or log(value)
![Page 15: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/15.jpg)
Avoiding artifacts
• Different system parameters may lead to different ads view– Browsers used different DNS servers– Browsers receive different cookies– HTTP proxy
![Page 16: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/16.jpg)
Analysis
• Configure two or more instances to differ by one parameter
• Comparing results for– Search Ads– Website Ads– Online Social Network Ads
![Page 17: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/17.jpg)
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
![Page 18: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/18.jpg)
Websites Ads
• Measure 15 websites that show Google ads• A, B: control in NY• C: SF; D: Germany• Location affects web ads
![Page 19: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/19.jpg)
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
![Page 20: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/20.jpg)
Online social network ads
• Set up three or more Facebook profiles• A, B: control and identical• C: differs from A by one profile parameter
![Page 21: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/21.jpg)
![Page 22: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/22.jpg)
![Page 23: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/23.jpg)
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
![Page 24: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/24.jpg)
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
![Page 25: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/25.jpg)
Ads differ by sexual preferences
![Page 26: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/26.jpg)
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
![Page 27: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/27.jpg)
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
![Page 28: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/28.jpg)
Question: how can we design a privacy-preserving online advertising system?
![Page 29: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/29.jpg)
Goals
• Support online advertising– A good revenue source to fund online services
• Preserve user privacy
![Page 30: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/30.jpg)
PrivAd
• Serving Ads from a localhost client• Actors: user, publisher, advertiser, broker, and
dealer
![Page 31: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/31.jpg)
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
![Page 32: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/32.jpg)
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
![Page 33: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/33.jpg)
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
![Page 34: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/34.jpg)
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
![Page 35: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/35.jpg)
Scaling PrivAd
• Ads churn is significant• 2GB/month of compressed ad data
![Page 36: Lecture 21: Privacy and Online Advertising](https://reader036.vdocuments.site/reader036/viewer/2022062520/56816337550346895dd3c754/html5/thumbnails/36.jpg)
Discussion
• What challenges does PrivAd may face in a practical deployment?