Privacy-Preserving Machine Learning: Methods, Challenges and Directions
- URL: http://arxiv.org/abs/2108.04417v1
- Date: Tue, 10 Aug 2021 02:58:31 GMT
- Title: Privacy-Preserving Machine Learning: Methods, Challenges and Directions
- Authors: Runhua Xu, Nathalie Baracaldo, James Joshi
- Abstract summary: Well-designed privacy-preserving machine learning (PPML) solutions have attracted increasing research interest from academia and industry.
This paper systematically reviews existing privacy-preserving approaches and proposes a PGU model to guide evaluation for various PPML solutions.
- Score: 4.711430413139393
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Machine learning (ML) is increasingly being adopted in a wide variety of
application domains. Usually, a well-performing ML model, especially, emerging
deep neural network model, relies on a large volume of training data and
high-powered computational resources. The need for a vast volume of available
data raises serious privacy concerns because of the risk of leakage of highly
privacy-sensitive information and the evolving regulatory environments that
increasingly restrict access to and use of privacy-sensitive data. Furthermore,
a trained ML model may also be vulnerable to adversarial attacks such as
membership/property inference attacks and model inversion attacks. Hence,
well-designed privacy-preserving ML (PPML) solutions are crucial and have
attracted increasing research interest from academia and industry. More and
more efforts of PPML are proposed via integrating privacy-preserving techniques
into ML algorithms, fusing privacy-preserving approaches into ML pipeline, or
designing various privacy-preserving architectures for existing ML systems. In
particular, existing PPML arts cross-cut ML, system, security, and privacy;
hence, there is a critical need to understand state-of-art studies, related
challenges, and a roadmap for future research. This paper systematically
reviews and summarizes existing privacy-preserving approaches and proposes a
PGU model to guide evaluation for various PPML solutions through elaborately
decomposing their privacy-preserving functionalities. The PGU model is designed
as the triad of Phase, Guarantee, and technical Utility. Furthermore, we also
discuss the unique characteristics and challenges of PPML and outline possible
directions of future work that benefit a wide range of research communities
among ML, distributed systems, security, and privacy areas.
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