Understanding the Network and User-Targeting Properties of Web Advertising Networks

1. Understanding the Network and User-Targeting Properties of Web Advertising Networks Yong Wang1,2 Daniel Burgener1 Aleksandar Kuzmanovic1 Gabriel Maciá-Fernández3 1UESTC (China) 2Northwestern University (USA) 3University of Granada (Spain)

2. Motivation “Online advertising is a $20 billion industry that is growing rapidly… It has become an integral and inseparable part of the World Wide Web” However, neither public auditing nor monitoring mechanisms still exist in this emerging area

3. Contributions • We present our initial efforts on building a network and content-level auditing service for Web-based ad networks. • Such an ad auditing service can effectively monitor and regulate ad industry. • Firstly, it helps potential new advertisers/publishers in the decision of choosing commissioners which better meet their requirements. • Secondly, it allows commissioners to evaluate their own networks, with the aim of detecting potential design flaws and points of failure with reduced quality of service.

4. Background Commissioner Publisher 2) Works with 1) Uploads ads 3) Provides scripts Advertiser … 5) Sends web pages’s content and scripts for ad Commissioner’s Ad servers 6) Fetches ad (and send cookies) 4) Fetches web page 7) Sends ad (and cookies) End user

5. Outline • Charting Online Advertising Network Infrastructure • Network-Level Performance • Content-Level Performance

6. Outline • Charting Online Advertising Network Infrastructure • Evaluation Platform • Candidates Selection • Finding Canonical Names • Mapping CNames to IP Addresses • Mapping IP Addresses to Locations • Network-Level Performance • Content-Level Performance

7. Evaluation Platform

8. Candidates Selection √ √ √

9. Finding Canonical Names AOL-Adtech : a627.g.akamai.net, a973.g.akamai.net, e1611.c.akamaiedge.net AOL-Tacoda : a1131.g.akamai.net, a1406.g.akamai.net, e922.p.akamaiedge.net AOL- Advertising:a949.g.akamai.net, a957.g.akamai, a1539.g.akamai.net, a1626.g.akamai.net, e1066.c.akamaiedge.net

10. Mapping CNames to IP Addresses The difference of the discovery capacity between two platforms 286 ÷306 = 93.5%

11. Mapping IP Addresses to Locations

12. Outline • Charting Online Advertising Network Infrastructure • Network-Level Performance • Delay Performance • Ad vs. Publisher Networks • Content-Level Performance

13. Delay for ad content servers Delay for ad DNS servers Delay Performance AOL/Akamai > Google > Adblade Ad content servers > Ad DNS servers

14. Commissioner Ad vs. Publisher Networks Ad network is worse Ad network is better In CDN case, Google-Google ≈ Publisher network In No-CDN case, Google-Google > Publisher network

15. Commissioner Ad vs. Publisher Networks Ad network is worse Ad network is better There exists no internal mechanism within a CDN to recognize and correct such anomalies. In CDN case, AOL-Adsonar ≈ Publisher network In No-CDN case, AOL-Adsonar > Publisher network

16. Commissioner Ad vs. Publisher Networks Ad networks is worse Ad network is worse Ad networks is better Ad network is better In CDN case, Adblade < Publisher network In No-CDN case, Adblade > Publisher network

17. Commissioner Ad vs. Publisher Networks The discrepancy between publishers’ and commissioners’ ad networks can be quite high. There exists no internal mechanism within a CDN to recognize and correct the huge discrepancy between commissioner and publisher network, even if both are served by the same CDN.

18. Outline • Charting Online Advertising Network Infrastructure • Network-Level Performance • Content-Level Performance • Distribution Mechanisms • Location-Based Advertising • Behavioral Targeting

19. Distribution Mechanisms (Similarity) Google-Google has a largepool of ads and distributes different adsinto different servers.

20. Distribution Mechanisms (Similarity) Adblade has a smaller pool of ads and puts all of them in the same machine (or a cluster of machines).

21. Distribution Mechanisms (Similarity) AOL uses different pools of ads depending on the location ofthe servers

22. Distribution Mechanisms Regional similarities in AOL-Adsonar AOL uses finer-grained location-based advertising, e.g., city-level advertising, in the U.S.

23. Location-Based Advertising Percentage of vantage points observing location-based ads Three commissioners deploy location-based advertising at various levels of granularity Google > Adblade > AOL CDN-based commissioners lag behind others in achieving finer-grained location-based advertising.

24. Behavioral Targeting Percentage increase of observed ’sport’ related ads when behavioral targeting is enabled (’local/uniform cookie’) compared with disabled (’no cookie’) Uniform cookie (enable cookies, and copy the cookies from one computer to all PL nodes, and then retrieving ads again to check whether profile data is stored locally or globally) Establish baseline (disable cookies, and access all websites, which may not be related to sports) Establish browsing pattern (enable cookies, and only visit websites fit in the category “sports”) Local cookie (enable cookies, and access all websites, which may not be related to sports in order to determine the difference when behavioral targeting is used) Data-center oriented commissioners are capable of collecting user profiles and applying behavioral targeting more effectively Both Google and AOL-Adsonar associate a user profile only with an ad server close to this user

25. Conclusions • We deployed an ad auditing service that can be universally applied to arbitrary commissioners’ networks. • Using this service, we performed an extensive network- and content-level analysis. • Our findings bringuseful auditing information to all entities involved in the onlineadvertising business.

26. Thank You