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Customized privacy preserving for classification based applications

Authors:

Yingshu LiAuthors Info & Claims

PAMCO '16: Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing

Pages 37 - 42

https://doi.org/10.1145/2940343.2940345

Published: 05 July 2016 Publication History

Abstract

The rise of sensor-equipped smart phones has enabled a variety of classification based applications that provide personalized services based on user data extracted from sensor readings. However, malicious applications aggressively collect sensitive information from inherent user data without permissions. Furthermore, they can mine sensitive information from user data just in the classification process. These privacy threats raise serious privacy concerns. In this paper, we introduce two new privacy concerns which are inherent-data privacy and latent-data privacy. We propose a framework that enables a data-obfuscation mechanism to be developed easily. It preserves latent-data privacy while guaranteeing satisfactory service quality. The proposed framework preserves privacy against powerful adversaries who have knowledge of users' access pattern and the data-obfuscation mechanism. We validate our framework towards a real classification-orientated dataset. The experiment results confirm that our framework is superior to the basic obfuscation mechanism.

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Cited By

He ZLi J(2019)Modeling SNP-Trait Associations and Realizing Privacy-Utility Tradeoff in Genomic Data PublishingBioinformatics Research and Applications10.1007/978-3-030-20242-2_6(65-72)Online publication date: 9-May-2019
https://doi.org/10.1007/978-3-030-20242-2_6
He ZCai ZYu J(2018)Latent-Data Privacy Preserving With Customized Data Utility for Social Network DataIEEE Transactions on Vehicular Technology10.1109/TVT.2017.273801867:1(665-673)Online publication date: Jan-2018
https://doi.org/10.1109/TVT.2017.2738018
He ZCai ZSun YLi YCheng X(2017)Customized privacy preserving for inherent data and latent dataPersonal and Ubiquitous Computing10.1007/s00779-016-0972-221:1(43-54)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00779-016-0972-2
Show More Cited By

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Published In

cover image ACM Conferences

PAMCO '16: Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing

July 2016

66 pages

ISBN:9781450343466

DOI:10.1145/2940343

General Chairs:
Xiuzhen Cheng
George Washington Univ
,
Zhenkai Liang
National University of Singapore, Singapore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 05 July 2016

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National Science Foundation

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MobiHoc'16

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MobiHoc'16: The Seventeenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

July 5 - 8, 2016

Paderborn, Germany

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Cited By

He ZLi J(2019)Modeling SNP-Trait Associations and Realizing Privacy-Utility Tradeoff in Genomic Data PublishingBioinformatics Research and Applications10.1007/978-3-030-20242-2_6(65-72)Online publication date: 9-May-2019
https://doi.org/10.1007/978-3-030-20242-2_6
He ZCai ZYu J(2018)Latent-Data Privacy Preserving With Customized Data Utility for Social Network DataIEEE Transactions on Vehicular Technology10.1109/TVT.2017.273801867:1(665-673)Online publication date: Jan-2018
https://doi.org/10.1109/TVT.2017.2738018
He ZCai ZSun YLi YCheng X(2017)Customized privacy preserving for inherent data and latent dataPersonal and Ubiquitous Computing10.1007/s00779-016-0972-221:1(43-54)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00779-016-0972-2
Biswas SSharif KLi FLiu Y(2017)3P Framework: Customizable Permission Architecture for Mobile ApplicationsWireless Algorithms, Systems, and Applications10.1007/978-3-319-60033-8_39(445-456)Online publication date: 27-May-2017
https://doi.org/10.1007/978-3-319-60033-8_39
Cai ZHe ZGuan XLi Y(2016)Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2016.2613521(1-1)Online publication date: 2016
https://doi.org/10.1109/TDSC.2016.2613521
Mao JWang RChen YXiao YJia YLiang Z(2016)A Function-Level Behavior Model for Anomalous Behavior Detection in Hybrid Mobile Applications2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)10.1109/IIKI.2016.2(497-505)Online publication date: Oct-2016
https://doi.org/10.1109/IIKI.2016.2

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