Paper Info
Title
Towards a Framework to Facilitate the Mobile Advertising Ecosystem
Abstract
To date, app developers are allowed to monetize their apps in two services: in-app advertising and in-app billing. Of these two, in-app billing is not prevalently used by users, whereas in-app advertising is considered an important funding source for developers. However, this service incurs a number of criticisms: (1) users must passively receive all mobile ads while using apps, (2) users get nothing from viewing or clicking ads, (3) ad networks transfer user private information to remote servers in an unencrypted format without user consent, and (4) negative impressions brought from irrelevant ads may harm the advertised brands. To overcome these problems, we propose In-App AdPay, a framework that combines the advantages of "in-app advertising" and "in-app billing" together so that ad networks can overtly ask users' permissions in order to serve more tailored ads, but in return, advertisers will pay targeted users' virtual transactions within the app (e.g., coins in mobile games) via a secure channel. While mobile users can be brought into the monetization loop, it will be technically and legitimately easier for ad networks to study users. We implemented the proof-of-concept framework and conducted a test with 42 volunteers. Based on these studies, we believe that "In-App AdPay" would balance user privacy and user experience without interfering with the existing monetization arrangements. Lastly, we reveal how tracked-by-consent users react in different test scenarios and value the permissions used in ad libraries.
Year
DOI
Venue
2016
10.1109/ICPADS.2016.0068
2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)
Keywords
Field
DocType
Mobile Advertising,Usable Privacy,Usability Testing & Surveys,User Interface,Unpaired Two-Sample T-Test
Secure channel,User experience design,Internet privacy,World Wide Web,Ask price,Computer science,Server,Monetization,Scenario testing,User interface,Private information retrieval,Distributed computing
Conference
ISSN
ISBN
Citations 
1521-9097
978-1-5090-5382-7
0
PageRank 
References 
Authors
0.34
17
3
Name
Order
Citations
PageRank
Gong Chen1588.46
Shouling Ji261656.91
John A. Copeland345660.84