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

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

doi:10.1145/3297280.3297399

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 conference › Paper › peer-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 language	English
Pages	1208-1215
Number of pages	8
DOIs	https://doi.org/10.1145/3297280.3297399
Publication status	Published - 2019 Jan 1
Event	34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus Duration: 2019 Apr 8 → 2019 Apr 12

Conference

Conference	34th Annual ACM Symposium on Applied Computing, SAC 2019
Country/Territory	Cyprus
City	Limassol
Period	19/4/8 → 19/4/12

Keywords

In-App Advertising
Mobile Contextual Advertising
Semantic Approach

ASJC Scopus subject areas

Software

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/3297280.3297399

Cite this

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title = "MOCA: A novel privacy-preserving contextual advertising platform on mobile devices",

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.",

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author = "Lee, {Jung Hyun} and Ryu, {Woo Jong} and Kim, {Kang Min} and Sang-Geun Lee",

year = "2019",

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doi = "10.1145/3297280.3297399",

language = "English",

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T2 - 34th Annual ACM Symposium on Applied Computing, SAC 2019

AU - Lee, Jung Hyun

AU - Ryu, Woo Jong

AU - Kim, Kang Min

AU - Lee, Sang-Geun

PY - 2019/1/1

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N2 - 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.

AB - 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.

KW - In-App Advertising

KW - Mobile Contextual Advertising

KW - Semantic Approach

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M3 - Paper

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ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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