Suman Nath
ABSTRACT
Advertising is the primary source of revenue for many mobile apps. One important goal of the ad delivery process is targeting users, based on criteria like users' geolocation, context, demographics, long-term behavior, etc. In this paper we report an in-depth study that broadly characterizes what targeting information mobile apps send to ad networks and how effectively, if at all, ad networks utilize the information for targeting users. Our study is based on a novel tool, called MadScope, that can (1) quickly harvest ads from a large collection of apps, (2) systematically probe an ad network to characterize its targeting mechanism, and (3) emulate user profiles of specific preferences and interests to study behavioral targeting. Our analysis of 500K ad requests from 150K Android apps and 101 ad networks indicates that apps do not yet exploit the full potential of targeting: even though ad controls provide APIs to send a lot of information to ad networks, much key targeting information is optional and is often not provided by app developers. We also use MadScope to systematically probe top 10 in-app ad networks to harvest over 1 million ads and find that while targeting is used by many of the top networks, there remain many instances where targeting information or behavioral profile does not have a statistically significant impact on how ads are chosen. We also contrast our findings with a recent study of targeted in-browser ads.
- P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, and S. Muthukrishnan. AdScape: harvesting and analyzing online display ads. In Int. conf. on WWW, 2014. Google Scholar
Digital Library
- R. Bhoraskar, S. Han, J. Jeon, T. Azim, S. Chen, J. Jung, S. Nath, R. Wang, and D. Wetherall. Brahmastra: Driving apps to test the security of third-party components. In USENIX Security, 2014. Google ScholarDigital Library
- C. Castelluccia, M. A. Kaafar, and T. Minh-Dung. Betrayed by Your Ads! In PETS- Privacy Enhancing Tools Symposium, 2012.Google Scholar
- X. Chen, A. Jindal, and Y. C. Hu. How much energy can we save from prefetching ads?: energy drain analysis of top 100 apps. In Proceedings of the Workshop on Power-Aware Computing and Systems (HotPower). ACM, 2013. Google ScholarDigital Library
- B. S. click-fraud with unwitting accessories. Mona gandhi and markus jakobsson and jacob ratkiewicz. Online Fraud, Part I Journal of Digital Forensic Practice, 1(2), 2006.Google Scholar
- J. Crussell, R. Stevens, and H. Chen. MAdFraud: Investigating ad fraud in android applications. In ACM MobiSys, 2014. Google ScholarDigital Library
- T. Danny. Flurry releases newest app use stats.myurlhttp://tech.co/flurry-app-stats-2014-09.Google Scholar
- D. S. Evans. The online advertising industry: Economics, evolution, and privacy. In Journal of Economic Perspectives, 2009.Google ScholarCross Ref
- A. Farahat and M. C. Bailey. How effective is targeted advertising? In Int. conf. on WWW, 2012. Google ScholarDigital Library
- GameHouse. Mobile advertising statistics ? 5 big trends you need to know!myurlhttp://partners.gamehouse.com/mobile-advertising-statistics-5-big-trends-need-know/.Google Scholar
- A. Goldfarb and C. Tucker. Online display advertising: Targeting and obtrusiveness. In Marketing Science, 2010. Google ScholarDigital Library
- M. Grace, W. Zhou, X. Jiang, and A. Sadeghi. Unsafe exposure analysis of mobile in-app advertisements. In Conference on Security and Privacy in Wireless and Mobile Networks (WiSEC), 2012. Google ScholarDigital Library
- S. Guha, B. Cheng, and P. Francis. Challenges in measuring online advertising systems. In ACM SIGCOMM Internet Measurement Conference, 2010. Google ScholarDigital Library
- S. Hao, B. Liu, S. Nath, W. G. Halfond, and R. Govindan. PUMA: Programmable ui-automation for large scale dynamic analysis of mobile apps. In ACM MobiSys, 2014. Google ScholarDigital Library
- A. J. Khan, K. Jayarajah, D. Han, A. Misra, R. Balan, and S. Seshan. CAMEO: A middleware for mobile advertisement delivery. In ACM MobiSys, 2013. Google ScholarDigital Library
- M. Lécuyer, G. Ducoffe, F. Lan, A. Papancea, T. Petsios, R. Spahn, A. Chaintreau, and R. Geambasu. Xray: Enhancing the web\textquoterights transparency with differential correlation. In USENIX Security, 2014.Google Scholar
- I. Leontiadis, C. Efstratiou, M. Picone, and C. Mascolo. Don't kill my ads!: balancing privacy in an ad-supported mobile application market. In Workshop on Mobile Computing Systems & Applications, page 2. ACM, 2012. Google ScholarDigital Library
- B. Liu, S. Nath, R. Govindan, and J. Liu. DECAF: Detecting and characterizing ad fraud in mobile apps. In USENIX NSDI, 2014. Google ScholarDigital Library
- P. Mohan, S. Nath, and O. Riva. Prefetching mobile ads: Can advertising systems afford it? In ACM EuroSys, 2013. Google ScholarDigital Library
- P. Pearce, A. Felt, G. Nunez, and D. Wagner. AdDroid: Privilege separation for applications and advertisers in android. In Symposium on Information, Computer and Communications Security, 2012. Google ScholarDigital Library
- T. Peterson. Google tests way to track consumers from mobile browsers to the apps they use.myurlhttp://adage.com/article/digital/google-tie-mobile-web-app-trackers-ad-targeting/294502/.Google Scholar
- L. Ravindranath, S. Nath, J. Padhye, and H. Balakrishnan. Automatic and scalable fault detection for mobile applications. In ACM MobiSys, 2014. Google ScholarDigital Library
- F. Roesner, T. Kohno, and D. Wetherall. Betrayed by Your Ads! In USENIX NSDI, 2012.Google Scholar
- S. Shekhar, M. Dietz, and D. S. Wallach. Adsplit: Separating smartphone advertising from applications. In USENIX Security Symposium, 2012. Google ScholarDigital Library
- A. N. Srivastava and M. Sahami. Text mining: classification, clustering, and applications. CRC Press, 2010. Google ScholarDigital Library
- R. Stevens, C. Gibler, J. Crussell, , J. Erickson, and H. Chen. Investigating user privacy in android ad libraries. In IEEE Mobile Security Technologies (MoST), 2012.Google Scholar
- I. Ullah, R. Boreli, D. Kaafar, and S. Kanhere. Characterising user targeting for in-app mobile ads. In INFOCOM International Workshop on Security and Privacy in Big Data (BigSecurity 2014), 2014.Google ScholarCross Ref
- N. Vallina-Rodriguez, J. Shah, A. Finamore, Y. Grunenberger, H. Haddadi, K. Papagiannaki, and J. Crowcroft. Breaking for commercials: Characterizing mobile advertising. In ACM SIGCOMM Internet Measurement Conference, 2012. Google ScholarDigital Library
- W3C. Same origin policy.myurlhttp://www.w3.org/Security/wiki/Same\_Origin\_Policy.Google Scholar
- J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Int. conf. on WWW, 2009. Google ScholarDigital Library
Index Terms
- MAdScope: Characterizing Mobile In-App Targeted Ads
Recommendations
MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
WWW '20: Proceedings of The Web Conference 2020Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable ...
Adscape: harvesting and analyzing online display ads
WWW '14: Proceedings of the 23rd international conference on World wide webOver the past decade, advertising has emerged as the primary source of revenue for many web sites and apps. In this paper we report a first-of-its-kind study that seeks to broadly understand the features, mechanisms and dynamics of display advertising ...
Estimating Demand for Mobile Applications in the New Economy
In 2013, the global mobile app market was estimated at over US$50 billion and is expected to grow to $150 billion in the next two years. In this paper, we build a structural econometric model to quantify the vibrant platform competition between mobile ...
Comments