MOCA: A novel privacy-preserving contextual advertising platform on mobile devices

Jung Hyun Lee, Woo Jong Ryu, Kang Min Kim, Sang-Geun Lee

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages1208-1215
Number of pages8
DOIs
Publication statusPublished - 2019 Jan 1
Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
Duration: 2019 Apr 82019 Apr 12

Conference

Conference34th Annual ACM Symposium on Applied Computing, SAC 2019
CountryCyprus
CityLimassol
Period19/4/819/4/12

Keywords

  • In-App Advertising
  • Mobile Contextual Advertising
  • Semantic Approach

ASJC Scopus subject areas

  • Software

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