Efficient Logistic Regression with Local Differential Privacy
- URL: http://arxiv.org/abs/2202.02650v1
- Date: Sat, 5 Feb 2022 22:44:03 GMT
- Title: Efficient Logistic Regression with Local Differential Privacy
- Authors: Guanhong Miao
- Abstract summary: Internet of Things devices are expanding rapidly and generating huge amount of data.
There is an increasing need to explore data collected from these devices.
Collaborative learning provides a strategic solution for the Internet of Things settings but also raises public concern over data privacy.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Internet of Things devices are expanding rapidly and generating huge amount
of data. There is an increasing need to explore data collected from these
devices. Collaborative learning provides a strategic solution for the Internet
of Things settings but also raises public concern over data privacy. In recent
years, large amount of privacy preserving techniques have been developed based
on differential privacy and secure multi-party computation. A major challenge
of collaborative learning is to balance disclosure risk and data utility while
maintaining high computation efficiency. In this paper, we proposed privacy
preserving logistic regression model using matrix encryption approach. The
secure scheme achieves local differential privacy and can be implemented for
both vertical and horizontal partitioning scenarios. Moreover, cross validation
is investigated to generate robust model results without increasing the
communication cost. Simulation illustrates the high efficiency of proposed
scheme to analyze dataset with millions of records. Experimental evaluations
further demonstrate high model accuracy while achieving privacy protection.
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