Characterising User Targeting For In-App Mobile Ads

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Ullah, Imdad; Boreli, Roksana; Kaafar, Dali; Kanhere, Salil

Ullah, Imdad; Boreli, Roksana; Kaafar, Dali; Kanhere, Salil


2014-04-27


Conference Material


INFOCOM International Workshop on Security and Privacy in Big Data (BigSecurity 2014)


Toronto, Canada


6


Targeted advertising is a growing area of interest in both business and research community. In mobile communications, related research works focus on the collection of user's personal information by the mobile apps, protection against such data collection, and the implications of additional traffic generated by the ads on the mobile device resource utilization. In this work, we present a novel analysis of targeted advertising in the Google AdMob advertising network and show insights about the relevance of Google user profiles, and the categories of apps used, on the in-app ads served on smartphones. We define the classes of ads based on the match between received ads and the app (contextual ads), and Google AdMob user's profile (targeted ads). Our analysis reveals that, for all comparable experiments, the proportion of targeted ads is in all cases higher than the proportion of contextual ads. Moreover, blocking the targeting (disabling targeting in an AdMob user profile settings) results in a significant drop in the number of received ads with some experimental instances receiving no ads at all. Overall, the number of targeted ads is comparatively lower than the number of generic ads. Although this could be partially due to the limited size of ad pools at the time of our experiments, there is also an indication that, although user's information is collected, the subsequent use of such information for ads is still low. We present additional insights on the comparison between Google AdMob and other mobile advertising networks and illustrate the dominance of the former in both the number of ads served and the time during which the ads are displayed to the mobile users.


Targeted Ads, Privacy, Mobile Apps, Experiments


http://www.ieee-infocom.org/


nicta:7898


Ullah, Imdad; Boreli, Roksana; Kaafar, Dali; Kanhere, Salil. Characterising User Targeting For In-App Mobile Ads. In: INFOCOM International Workshop on Security and Privacy in Big Data (BigSecurity 2014); Toronto, Canada. 2014-04-27. 6.



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