Enabling privacy-assured mobile advertisement targeting and dissemination

Zhenkui Shi, Xiaoning Liu, Xingliang Yuan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

Abstract

With the fast growing market of mobile applications, mobile advertising attracts wide attention from both business and research communities in recent years. Targeted mobile advertising aims to analyze user profile and explore user interests so as to deliver ads to potentially interested users and maximize revenue. However, collecting user personal information raises severe privacy concerns. In this paper, we propose a practical targeted mobile advertising service framework while preserving user privacy and enabling accurate targeting. In particular, this framework enables accurate and private user targeting through a privacy-preserving matrix factorization protocol via homomorphic operations. To achieve private ads dissemination, it further adopts the latest advancement of private information retrieval (PIR) to allow the users to obtain accurate ratings and retrieve the most relevant ads without revealing their profiles and accessed encrypted ads. Security and cost analysis are conducted to show that our design achieves strong security guarantees with practical performance.

Original languageEnglish
Title of host publicationSCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing
EditorsCong Wang, Murat Kantarcioglu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages51-57
Number of pages7
ISBN (Print)9781450349703
DOIs
Publication statusPublished - 2 Apr 2017
Externally publishedYes
EventACM International Workshop on Security in Cloud Computing 2017 - Abu Dhabi, United Arab Emirates
Duration: 2 Apr 20172 Apr 2017
Conference number: 5th
https://dl-acm-org.ezproxy.lib.monash.edu.au/citation.cfm?id=3055259&picked=prox (Proceedings)

Workshop

WorkshopACM International Workshop on Security in Cloud Computing 2017
Abbreviated titleSCC 2017
CountryUnited Arab Emirates
CityAbu Dhabi
Period2/04/172/04/17
Internet address

Keywords

  • Matrix factorization
  • Mobile targeted advertising
  • PIR
  • Privacy preserving

Cite this

Shi, Z., Liu, X., & Yuan, X. (2017). Enabling privacy-assured mobile advertisement targeting and dissemination. In C. Wang, & M. Kantarcioglu (Eds.), SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing (pp. 51-57). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3055259.3055269
Shi, Zhenkui ; Liu, Xiaoning ; Yuan, Xingliang. / Enabling privacy-assured mobile advertisement targeting and dissemination. SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing. editor / Cong Wang ; Murat Kantarcioglu. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 51-57
@inproceedings{93e9a1cf98ee4435bb8ac874b9274e99,
title = "Enabling privacy-assured mobile advertisement targeting and dissemination",
abstract = "With the fast growing market of mobile applications, mobile advertising attracts wide attention from both business and research communities in recent years. Targeted mobile advertising aims to analyze user profile and explore user interests so as to deliver ads to potentially interested users and maximize revenue. However, collecting user personal information raises severe privacy concerns. In this paper, we propose a practical targeted mobile advertising service framework while preserving user privacy and enabling accurate targeting. In particular, this framework enables accurate and private user targeting through a privacy-preserving matrix factorization protocol via homomorphic operations. To achieve private ads dissemination, it further adopts the latest advancement of private information retrieval (PIR) to allow the users to obtain accurate ratings and retrieve the most relevant ads without revealing their profiles and accessed encrypted ads. Security and cost analysis are conducted to show that our design achieves strong security guarantees with practical performance.",
keywords = "Matrix factorization, Mobile targeted advertising, PIR, Privacy preserving",
author = "Zhenkui Shi and Xiaoning Liu and Xingliang Yuan",
year = "2017",
month = "4",
day = "2",
doi = "10.1145/3055259.3055269",
language = "English",
isbn = "9781450349703",
pages = "51--57",
editor = "Cong Wang and Murat Kantarcioglu",
booktitle = "SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

Shi, Z, Liu, X & Yuan, X 2017, Enabling privacy-assured mobile advertisement targeting and dissemination. in C Wang & M Kantarcioglu (eds), SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing. Association for Computing Machinery (ACM), New York NY USA, pp. 51-57, ACM International Workshop on Security in Cloud Computing 2017, Abu Dhabi, United Arab Emirates, 2/04/17. https://doi.org/10.1145/3055259.3055269

Enabling privacy-assured mobile advertisement targeting and dissemination. / Shi, Zhenkui; Liu, Xiaoning; Yuan, Xingliang.

SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing. ed. / Cong Wang; Murat Kantarcioglu. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 51-57.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

TY - GEN

T1 - Enabling privacy-assured mobile advertisement targeting and dissemination

AU - Shi, Zhenkui

AU - Liu, Xiaoning

AU - Yuan, Xingliang

PY - 2017/4/2

Y1 - 2017/4/2

N2 - With the fast growing market of mobile applications, mobile advertising attracts wide attention from both business and research communities in recent years. Targeted mobile advertising aims to analyze user profile and explore user interests so as to deliver ads to potentially interested users and maximize revenue. However, collecting user personal information raises severe privacy concerns. In this paper, we propose a practical targeted mobile advertising service framework while preserving user privacy and enabling accurate targeting. In particular, this framework enables accurate and private user targeting through a privacy-preserving matrix factorization protocol via homomorphic operations. To achieve private ads dissemination, it further adopts the latest advancement of private information retrieval (PIR) to allow the users to obtain accurate ratings and retrieve the most relevant ads without revealing their profiles and accessed encrypted ads. Security and cost analysis are conducted to show that our design achieves strong security guarantees with practical performance.

AB - With the fast growing market of mobile applications, mobile advertising attracts wide attention from both business and research communities in recent years. Targeted mobile advertising aims to analyze user profile and explore user interests so as to deliver ads to potentially interested users and maximize revenue. However, collecting user personal information raises severe privacy concerns. In this paper, we propose a practical targeted mobile advertising service framework while preserving user privacy and enabling accurate targeting. In particular, this framework enables accurate and private user targeting through a privacy-preserving matrix factorization protocol via homomorphic operations. To achieve private ads dissemination, it further adopts the latest advancement of private information retrieval (PIR) to allow the users to obtain accurate ratings and retrieve the most relevant ads without revealing their profiles and accessed encrypted ads. Security and cost analysis are conducted to show that our design achieves strong security guarantees with practical performance.

KW - Matrix factorization

KW - Mobile targeted advertising

KW - PIR

KW - Privacy preserving

UR - http://www.scopus.com/inward/record.url?scp=85022214074&partnerID=8YFLogxK

U2 - 10.1145/3055259.3055269

DO - 10.1145/3055259.3055269

M3 - Conference Paper

SN - 9781450349703

SP - 51

EP - 57

BT - SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing

A2 - Wang, Cong

A2 - Kantarcioglu, Murat

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

ER -

Shi Z, Liu X, Yuan X. Enabling privacy-assured mobile advertisement targeting and dissemination. In Wang C, Kantarcioglu M, editors, SCC' 17 - Proceedings of the 5th ACM International Workshop on Security in Cloud Computing. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 51-57 https://doi.org/10.1145/3055259.3055269