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MoCA: a novel privacy-preserving contextual advertising platform on mobile devices

Published: 08 April 2019 Publication History

Abstract

In this work, we propose a novel contextual advertising platform, called MoCA, which is designed to improve the relevance of in-app advertising in a stand-alone, privacy-protecting manner on mobile devices. MoCA understands the semantics of the current app page and matches semantically relevant ads inside mobile devices. In addition, MoCA controls the degree of privacy protection per user by utilizing a novel semantic generalization model on top of topical taxonomy. Our experimental results verify the effectiveness and feasibility of MoCA with minimal system overheads in terms of runtime, memory usage, and energy consumption. To the best of our knowledge, this is one of few work on the mobile contextual advertising platform without resort to ad servers.

References

[1]
Andrei Broder, Marcus Fontoura, Vanja Josifovski, and Lance Riedel. 2007. A Semantic Approach to Contextual Advertising. In Proc. the International ACM SIGIR Conference on Research and Development in Information Retrieval. 559--566.
[2]
Jonathan Crussell, Ryan Stevens, and Hao Chen. 2014. MAdFraud: Investigating Ad Fraud in Android Applications. In Proc. the International Conference on Mobile Systems, Applications, and Services. 123--134.
[3]
eMarketer. 2015. https://www.emarketer.com/Report/US-Ad-Spending-eMarketers-Updated-Estimates-Forecast-20152020/2001915.
[4]
Michael C. Grace, Wu Zhou, Xuxian Jiang, and Ahmad-Reza Sadeghi. 2012. Unsafe Exposure Analysis of Mobile In-app Advertisements. In Proc. the ACM Conference on Security and Privacy in Wireless and Mobile Networks. 101--112.
[5]
JongWoo Ha, Jung-Hyun Lee, Won-Jun Jang, Yong-Ku Lee, and SangKeun Lee. 2014. Toward robust classification using the Open Directory Project. In Proc. the International Conference on Data Science and Advanced Analytics. 607--612.
[6]
JongWoo Ha, Jung-Hyun Lee, and SangKeun Lee. 2014. EPE: An Embedded Personalization Engine for Mobile Users. IEEE Internet Computing 18, 1 (Janurary/February 2014), 30--37.
[7]
Eui-Hong Han and George Karypis. 2000. Centroid-based Document Classification: Analysis and Experimental Results. In Proc. European Conference on Principles of Data Mining and Knowledge Discovery. 424--431.
[8]
Michaela Hardt and Suman Nath. 2012. Privacy-aware Personalization for Mobile Advertising. In Proc. the ACM Conference on Computer and Communications Security. 662--673.
[9]
Azeem J. Khan, Kasthuri Jayarajah, Dongsu Han, Archan Misra, Rajesh Balan, and Srinivasan Seshan. 2013. CAMEO: A Middleware for Mobile Advertisement Delivery. In Proc. the International Conference on Mobile Systems, Applications, and Services. 125--138.
[10]
Byoungjip Kim, Jin-Young Ha, SangJeong Lee, Seungwoo Kang, Youngki Lee, Yunseok Rhee, Lama Nachman, and Junehwa Song. 2011. AdNext: A Visit-pattern-aware Mobile Advertising System for Urban Commercial Complexes. In Proc. the Workshop on Mobile Computing Systems and Applications. 7--12.
[11]
Jung-Hyun Lee, JongWoo Ha, Jin-Yong Jung, and SangKeun Lee. 2013. Semantic Contextual Advertising Based on the Open Directory Project. ACM Transactions on the Web 7, 4 (October 2013), 24:1--24:22.
[12]
Jung-Hyun Lee, So-Young Jun, So-Jung Park, Kang-Min Kim, and SangKeun Lee. 2017. Demo: Mobile Contextual Advertising Platform Based on Tiny Text Intelligence. In Proc. the International Conference on Mobile Systems, Applications, and Services. 181.
[13]
Bin Liu, Suman Nath, Ramesh Govindan, and Jie Liu. 2014. DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps. In Proc. the USENIX Conference on Networked Systems Design and Implementation. 57--70.
[14]
Prashanth Mohan, Suman Nath, and Oriana Riva. 2013. Prefetching Mobile Ads: Can Advertising Systems Afford It?. In Proc. the ACM European Conference on Computer Systems. 267--280.
[15]
Suman Nath. 2015. MAdScope: Characterizing Mobile In-App Targeted Ads. In Proc. the International Conference on Mobile Systems, Applications, and Services. 59--73.
[16]
Suman Nath, Felix Xiaozhu Lin, Lenin Ravindranath, and Jitendra Padhye. 2013. SmartAds: Bringing Contextual Ads to Mobile Apps. In Proc. the International Conference on Mobile Systems, Applications, and Services. 111--124.
[17]
PageFair. 2016. http://www.theverge.com/2016/5/31/11817476/mobile-ad-block-smartphone-pagefair-report.
[18]
So-Jung Park, Jung-Hyun Lee, So-Young Jun, Kang-Min Kim, and SangKeun Lee. 2017. MoCA+: Incorporating User Modeling into Mobile Contextual Advertising. In Proc. the ACM/IFIP/USENIX International Conference on Middleware. 21--22.
[19]
Gerard M Salton, Andrew Wong, and Chungshu Yang. 1975. A Vector Space Model for Automatic Indexing. Commun. ACM 18, 11 (November 1975), 613--620.
[20]
Shashi Shekhar, Michael Dietz, and Dan S. Wallach. 2012. AdSplit: Separating Smartphone Advertising from Applications. In Proc. the USENIX Conference on Security Symposium. 553--567.
[21]
Imdad Ullah, Roksana Boreli, Mohamed Ali Kaafar, and Salil S. Kanhere. 2014. Characterising user targeting for in-App Mobile Ads. In Proc. the IEEE Conference on Computer Communications Workshops. 547--552.
[22]
Jun Yan, Ning Liu, Gang Wang, Wen Zhang, Yun Jiang, and Zheng Chen. 2009. How Much Can Behavioral Targeting Help Online Advertising?. In Proc. the International Conference on World Wide Web. 261--270.
[23]
Wen-tau Yih, Joshua Goodman, and Vitor R. Carvalho. 2006. Finding Advertising Keywords on Web Pages. In Proc. the International Conference on World Wide Web. 213--222.

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      cover image ACM Conferences
      SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
      April 2019
      2682 pages
      ISBN:9781450359337
      DOI:10.1145/3297280
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      Published: 08 April 2019

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      Author Tags

      1. in-app advertising
      2. mobile contextual advertising
      3. semantic approach

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      • Korea government (MSIT)

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      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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