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Adnostic: Privacy Preserving Targeted Advertising

Adnostic: Privacy Preserving Targeted Advertising. Rachel Stonehirsch and Nan Wu. Online Behavioral Advertising. Track users across web sites to infer user interests and preferences Better ad placement Not a new practice 1990s: DoubleClick used 3rd party cookies to track users. Parties.

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Adnostic: Privacy Preserving Targeted Advertising

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  1. Adnostic: Privacy Preserving Targeted Advertising Rachel Stonehirsch and Nan Wu

  2. Online Behavioral Advertising • Track users across web sites to infer user interests and preferences • Better ad placement • Not a new practice • 1990s: DoubleClick used 3rd party cookies to track users

  3. Parties • Advertiser • Has an online ad to embed in web pages • Publisher • Owns web pages and is willing to place ads from others • Ad-network • Collects ads/payment from advertisers • Places ads on publisher pages

  4. Parties • Content Distribution Network(CDN) - "Collude" with ad network • Trusted third party - Cryptographic service provider - Not "collude" with any other parties

  5. Tracking • Associate an identifier with a user • Cookies, IP address and User-Agent strings

  6. Privacy • Privacy and consumer advocacy groups concerned about how OBA affects privacy • Argument: • Behavioral targeting is inherently in conflict with privacy • Our work shows that it is possible to have effective targeted advertising and still preserve privacy

  7. Privacy Threat s • Clickstream • Behavioral profile • Ad impression history • Ad click history

  8. Adnostic • A Firefox extension • Uses browsing history database • Runs behavioral targeting algorithm in browser • User information not leaked outside the browser Motivation: A complement, not a replacement

  9. Adnostic • Cryptographic techniques for accurate billing • Only click history is provided to ad network • Against click fraud scams • Available from advertisers

  10. Why adnostic? • Pleasure privacy-conscious publishers • More visibility • Maybe better than user tracking • Private browsing mode • User control • Standardized segmentation

  11. Adnostic Architecture: Targeting with Privacy • Behavior profiling • Ad insertion • Accounting

  12. Behavioral Profiling • Continually updates interest categorizations • More than interest: intent and influence • User sessions: keystroke dynamic or last few pages viewed

  13. Ad Insertion • Ad-network detects Adnostic • A list of n ads is send back, each with a classification • One of n ads is chosen to display

  14. Billing: Charge per Click Model • Users click on ad and re-directed to advertiser's site • Billing takes place directly at the site

  15. Billing: Charge per Impression Model • N ads are pushed to the browser • One ad is displayed to user • One advertiser is charged • How can the ad-network charge the correct advertiser without knowing which ad was displayed? • Solution: • Additively homomorphic encryption • Zero knowledge proofs

  16. Homomorphic Encryption • Given public key pk • Given ciphertexts E(pk, x1) and E(pk, x2) • Can create ciphertext E(pk, x1 + x2) • Can create ciphertext E(pk, c*x) for any scalar c

  17. Billing: Initialization • —Ad-network identifies ad by an ID • —Ad-network stores each ad and encrypted counter, CID • —When ad is first uploaded • CID E(pk, 0)

  18. Billing: Ad Insertion • Ad-network sends pk and n ads to browser • (pk, ad1, ad2, ...,adn) • Browser chooses ad to display to user • Creates binary vector v with n components • Encrypt each element of v using pk and send to ad-network with zero-knowledge proofs • (E(pk, v1),...,E(pk,vn))

  19. Billing: Ad Insertion • Ad-network multiplies vector by c • (E(pk, c*v1),...E(pk,c*vn)) • Ad-network adds encrypted vector values to each ad's encrypted counter • Result: Quantity c is added to counter of ad displayed

  20. Billing: Settlement • Ad-network sends encrypted counters to a trusted third party (TTP) • TTP decrypts counters and sends response to ad network

  21. Implementation • User Profiling Module • Monitors browsing activity to build a list of user interests • Ad Rendering Module • Selects ads based on user profile • Inserts ads into the web pages

  22. Implementation: User Profiling • Adnostic extracts keywords from the page meta-data and the URL • List of keywords used to retrieve categories related to page content • Categories derived from all pages visited used to make up profile

  23. Implementation: In-Browser Categorization • Adnostic comes with: • List of categories • Cosine-similarity matrix • Used to compute categories for a list of keywords obtained from a web page

  24. Implementation: Ad Rendering • Ad-network sends to the browser: • List of behavioral categories • A score representing relevancy of the ad • For each extension any numerical parameters that the extension accepts • Browser creates combined score for each ad • Uses score sent by ad-network • Uses how well list of categories match the user's profile

  25. Implementation: Ad Rendering • adnostic.render() • Attributes are an id, url, and targeting inputs described earlier, height and width parameters, and cryptographic key • Browser creates n DOM elements • All ads are downloaded • Only one is displayed to the user

  26. Evaluation • Based on advertisement rendering delay • Observe impact on page loading time • Websites can • Publish many ads • Intensively use scripts • Include external elements that take time to load • Adnostic increases loading dealy • Might be negligible on heavy websites • Might affect lightweight websites

  27. Evaluation • SlashDot • Lightweight website (3 banner ads) • ReadWriteWeb • Heavy website( 13 banner ads and content from external websites) • WeSecretSoftwareClub • Lightweight website (3 text ads) • TheRegister • Publishes text ads and banners.

  28. Evaluation: Ad Rendering Time • Website 3 achieves fastest rendering time • Publishes only text ads • Faster when 10 text ads are downloaded • Time increases when banner ad are displayed • Time to download 10 banner is similar to time to download 20 text ads

  29. Evaluation: Page Loading Time • In general, impact on loading time was low • Website 2 • Includes external content and publishes many ads • To load page, browser opens many connections • Firefox limits number of simultaneous connections • Solution: • Increase number of simultaneous connections • Degrade browsing experience • Alternative: Fetch n ads via a single HTTP request

  30. Conclusion • Address issues between tension surrounding behavioral targeting and user privacy • Primary goal: Create a system that would preserve user privacy and still serve ads effectively • Complement existing ad infrastructure not replace it

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