1. Understanding the Network and User-Targeting Properties of Web Advertising Networks Yong Wang 1,2 Daniel Burgener 1 Aleksandar Kuzmanovic 1 Gabriel Maciá-Fernández 3 1 UESTC (China) 2 Northwestern University (USA) 3 University of Granada (Spain)

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

3. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Publisher Background 1) Uploads ads 2) Works with 4) Fetches web page 5) Sends web pages’s content and scripts for ad 7) Sends ad (and cookies) 3) Provides scripts Advertiser Commissioner End user … Commissioner’s Ad servers 6) Fetches ad (and send cookies)

5. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Outline Charting Online Advertising Network Infrastructure Network-Level Performance Content-Level Performance

6. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Evaluation Platform Open Recursive DNSPlanetLab RegionCountriesServers % of total CountriesServers % of total N. America253364543.73313949.29 Europe502629434.182210336.53 Asia401401918.2272910.28 S. America1214051.83382.84 Oceania811111.44131.06 Africa244560.60000.0 Total15976930100.0036282100.00

8. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Candidates Selection √ √ √

9. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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 CommissionersSub-companiesRepresentative domains Google Google-Googlepagead.1.google.com Google-Doubleclickpagead.1.doubleclick.net AOL/ Akamai AOL-Adtech3 AOL- Adsonara950.g.akamai.net AOL- Tacoda3 AOL- Advertising5 Adbladeweb.adblade.com

10. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Mapping CNames to IP Addresses Commissioners # of IP Ad content serversAd DNS servers Google3066 AOL/Akamai111328361 Adblade12 The difference of the discovery capacity between two platforms 286 ÷306 = 93.5%

11. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Mapping IP Addresses to Locations Region # of IP GoogleAOL/AkamaiAdblade AdDNSAdDNSAdDNS N. America15436761542612 Europe7023017182400 Asia24199488300 S. America1401449100 Oceania0017812400 Africa00383300 Unknowns2400000 Total286011132838112

12. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Outline Charting Online Advertising Network Infrastructure Network-Level Performance –Delay Performance –Ad vs. Publisher Networks Content-Level Performance

13. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Delay Performance Delay for ad content servers AOL/Akamai > Google > Adblade Ad content servers > Ad DNS servers Delay for ad DNS servers

14. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Commissioner Ad vs. Publisher Networks In CDN case, AOL-Adsonar ≈ Publisher network In No-CDN case, AOL-Adsonar > Publisher network Ad network is worse Ad network is better There exists no internal mechanism within a CDN to recognize and correct such anomalies.

16. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Commissioner Ad vs. Publisher Networks Ad networks is worse Ad networks is better In CDN case, Adblade < Publisher network In No-CDN case, Adblade > Publisher network Ad network is worse Ad network is better

17. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Outline Charting Online Advertising Network Infrastructure Network-Level Performance Content-Level Performance –Distribution Mechanisms –Location-Based Advertising –Behavioral Targeting

19. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Distribution Mechanisms (Similarity) Google-Google has a large pool of ads and distributes different ads into different servers.

20. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Distribution Mechanisms (Similarity) AOL uses different pools of ads depending on the location of the servers

22. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Distribution Mechanisms Regional similarities in AOL-Adsonar AOL uses finer-grained location-based advertising, e.g., city- level advertising, in the U.S.

23. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Location-Based Advertising Percentage of vantage points observing location-based ads Three commissioners deploy location-based advertising at various levels of granularity CommissionerCity (%)State (%)No info (%) Google31.5821.9346.49 AOL-Adsonar8.0012.0080.00 Adblade37.310.0062.69 Google > Adblade > AOL CDN-based commissioners lag behind others in achieving finer-grained location-based advertising.

24. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks Behavioral Targeting Percentage increase of observed ’sport’ related ads when behavioral targeting is enabled (’local/uniform cookie’) compared with disabled (’no cookie’) CommissionerLocal cookie (%)Uniform cookie (%) Google253 AOL-Adsonar135 Adblade00 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 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) 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)

25. Yong Wang Understanding the Network and User-Targeting Properties of Web Advertising Networks 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 bring useful auditing information to all entities involved in the online advertising business.

26. Thank You