1. Itay Gonshorovitz Foundation of privacy Targeted Online Advertising

2. Topics  Introduction to online advertisement  Understanding the participants and their roles.  Targeted advertising.  Privacy Issues  Solutions  User based solutions  Collaborative solutions  Conclusions

3. Introduction  Online Advertising plays a critically important role in the Internet world.  advertising is the main way of profiting from the Internet, the history of Internet advertising developed alongside the growth of the medium itself

4. Facts and short history  First internet banner, 1994, AT&T.  Also in 1994, the first commercial spam, a "Green Card Lottery".  The first ad server was developed by FocaLink Media Services and introduced on 1995.  In March 2008, Google acquired DoubleClick for US$3.1 billion in cash.

5. Parties  Advertiser  Got money, wants publicity  e.g., Coca-Cola  Publisher  Got content, wants money  Cnn.com  Ad-network  Got advertising infrastructure, wants money  e.g., Google AdSense, Yahoo  Consumer  Wants free content

6. Ad embedding

7. Business Model  CPM = Cost Per thousand impressions  Impression: user just sees the ad.  Rates vary from $0.25 to $100  CPC = Cost Per Click  This is the cost charged to an advertiser every time their ad is "clicked" on  Rates around 0.3$ per click

8. Click fraud  clicking on an ad for the purpose of generating a charge per click without having actual interest.  Might be:  The publisher  Advertiser’s competitor  The publisher’s competitor  Ad-networks deal with it by trying to identify who clicks on the ads.

9. Online behavioral advertising  Online behavioral advertising refers to the practice of ad-networks tracking users across web sites in order to learn user interests and preferences.  Benefits  Advertisers targets a more focused audience which increases the effectively.  Consumer is “bothered” by more relevant and interesting ads.

10. How ad-networks match ads  Most behavioral targeting systems work by categorizing users into one or more audience segments.  Profiling users based on collected data  Search history – analyzing search keywords  Browse history - analyzing content of visited pages  Purchase history  Social networks  Geography

11. How Ad-Networks track users  Cookies  3 rd Party cookies  Flash cookies  Web bug  IP address  User-agent Headers  Browser + OS  More than 24,000 signatures

12. Levis.com case study

14. Privacy  Tracking and categorizing users by the ad-networks tend to violate user’s privacy.  The gathered information, linked with the users real identity, form a violation of privacy in its most basic form.  For example, if a person is searching the web for information on a serious genetic disease, that information can be collected and stored along with that consumer's other information - including information that can uniquely identify the consumer.

15. So… What we have so far?  User - Preserve his privacy  Ad-Network & Publisher –  Maintain targeting and preserve their effectiveness and income  Still want to be able to fight click fraud  Questions:  Do the two goals necessarily conflict?  Or can they be both achieved?

16. Naive (paranoid) solution  Surf only across anonymizing proxies.  TOR  Surf in private mode  Advantages  Effective from the user’s perspective.  Disadvantages  Are proxies really anonymizing?  Very awkward  Slower  Damages targeted advertising

17. TrackMeNot (Howe, Nissenbaum, 2005)  Implemented as a Firefox plugin.  Achieves privacy through obfuscation.  Generates noisy queries.  Starts with fixed a seed query list and evolve queries base on previous results.  Mimics user behavior so fake queries be indistinguishable:  Query timing  Click through behavior

18. TrackMeNot  Advantages  Simple  Disadvantages  Still the real queries can be connected to real identity.  Might have problems with offensive contents.  Again, damages targeted advertising

19. Privad (Guha, Reznichenko, Tang, et al., 2009)  Require client software:  saves locally database of ads (served by the ad-network)  Learn user interests in order to match ads.  Match add from the local database according to the User interests.

20. Privad  Introduce new party – Dealer:  Proxies anonymously all communication between the user and the ad-network.  might be government regulatory agency.  hides user’s identity from the ad-network, but itself does not learn any profile information about the user since all messages between the user and ad-network are encrypted.

22. Privad  Advantages  Ad-Networks can still target ads without violates user privacy.  Disadvantages  Complicated to add the new party.  Ad-Network has to trust the dealer in order to fight click-fraud which might unmotivated them to cooperate.

23. Adnostic (Toubina, Narayanan, Boneh, et al., 2009)  Two party solution:  Client side: Implemented as a Firefox plugin.  Server side: requires Ad-Network support  User’s preferences and interests are stored locally by the plugin, instead of at the Ad-network.  The targeted ad is selected by the plugin locally at the users computer, instead of at the Ad-Network servers.

24. Adnostic - Accounting  “charge per click” model remains unchanged.  “charge per impression” is harder.  It uses homomorphic encryption scheme.  given the public key and ciphertexts, anyone can calculate  given the public key and ciphertexts, and scalar c, can be calculated.

25. Adnostic - charge per impression protocol  Client: Track user activity and maintains the data locally.  Visits an Ad supported website.  Server: Sends a list of n ads ids along with public key  The browser chooses an ad to display to the user. Then creates that matches the selected ad, then send, Along with zero-knowledge proof that and each is 0 or 1.

26. Adnostic - charge per impression protocol  Validates the proof. If the proof is valid then using homomorphic encryption calculates when c is the price of viewing the ad.  The server save encrypted counter for each ad and add to it the previous values. Only one counter’s real value change.  At the end of the billing period, say a month, each counter is decrypted (should be done by trusted authority) and the advertisers pays for the ad- network.

27. Adnostic  Advantages  Ad-networks can still target ads without violates user privacy.  Ad-networks can still detect click fraud though it will be difficult without gathering information on IP even for a short time.  Disadvantages  Ad-networks become weaker.  Ad-networks can still track user if they are willing to, and the protocol is built on trust.

28. Conclusions  In my opinion, It is hard to believe that ad-networks will give up the power of tracking users without legislation.  Nevertheless, There are reasonable solutions that still support targeted advertising without violating users privacy.

29. Questions?

30. References  [1] Daniel c. Howe and Helen Nissenbaum. Trackmenot: resisting surveillance in web search. 2005.  [2] Saikat Guha, Bin Cheng, Alexey Reznichenko, Hamed Haddadi, and Paul Francis. Privad: Rearchitecting online advertising for privacy. 2009.  [3] Vincent Toubiana, Arvind Narayanan Dan Boneh, Helen Nissenbaum, and Solon Barocas. Adnostic: Privacy preserving targeted advertising. 2009.  [4] Catherine Dwyer. Behavioral targeting: A case study of consumer tracking on levis.com. In 15th Americas Conference on Information Systems, 2009..