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MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING

Abstract: 

The rapid rise of IoT and Big Data can facilitate the use of data to enhance our quality of life. However, the omnipresent and sensitive nature of data can simultaneously generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve the intended purposes, but not for prying into one’s sensitive information. We address this challenge via utility maximizing lossy compression of data. Our techniques combine the mathematical rigor of Kernel Learning models with the structural richness of Deep Neural Networks, and lead to the novel Multi-Kernel Learning and Hybrid Learning models. We systematically construct the proposed models in progressive stages, as motivated by the cumulative improvement in the experimental results from the two previously non-intersecting regimes, namely, Kernel Learning and Deep Neural Networks. The
final experimental results of the three proposed models on three mobile sensing datasets show that, not only are our methods able to improve the utility prediction accuracies, but they can also cause sensitive predictions to perform nearly as bad as random guessing, resulting in a win-win situation in terms of utility and privacy.

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Paper Details

Authors:
Mert Al, Thee Chanyaswad, Sun-Yuan Kung
Submitted On:
13 April 2018 - 3:55pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Mert Al
Paper Code:
MLSP-P11.9
Document Year:
2018
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Document Files

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[1] Mert Al, Thee Chanyaswad, Sun-Yuan Kung, "MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2756. Accessed: Oct. 16, 2018.
@article{2756-18,
url = {http://sigport.org/2756},
author = {Mert Al; Thee Chanyaswad; Sun-Yuan Kung },
publisher = {IEEE SigPort},
title = {MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING},
year = {2018} }
TY - EJOUR
T1 - MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING
AU - Mert Al; Thee Chanyaswad; Sun-Yuan Kung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2756
ER -
Mert Al, Thee Chanyaswad, Sun-Yuan Kung. (2018). MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING. IEEE SigPort. http://sigport.org/2756
Mert Al, Thee Chanyaswad, Sun-Yuan Kung, 2018. MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING. Available at: http://sigport.org/2756.
Mert Al, Thee Chanyaswad, Sun-Yuan Kung. (2018). "MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING." Web.
1. Mert Al, Thee Chanyaswad, Sun-Yuan Kung. MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2756