Research Topic

Secure Privacy-preserving Machine Learning

About this Research Topic

Machine learning algorithms, such as deep learning algorithms, have attracted attention as a breakthrough in computer vision, speech recognition, and other areas. However, machine learning algorithms require access to raw data which is often privacy sensitive. On the other hand, machine learning systems can be fragile and easily fooled by attacks that are effective across different architectures and applications. A novel combination of techniques is needed to enable secure privacy-preserving machine learning. Different approaches based on differential privacy-like techniques, homomorphic encryption where operations are performed on encrypted data, and techniques from the field of secure multi-party computation are being developed to deal with this problem.

The aim of this Article Collection is to bring together world-leading research on issues related to security and privacy of machine learning and data analytics and serve as a forum to unify different perspectives on this problem and explore the relative merits of each approach.

We invite submissions of novel research from academic researchers, practitioners and industry, both theory and application-oriented, on secure privacy-preserving machine learning and data analytics. The topics include but not limited to:

• Secure multi-party computation techniques for machine learning
• Homomorphic encryption techniques for machine learning
• Differential privacy techniques for machine learning
• Machine learning on encrypted data
• Adversarial machine learning (attacks and defenses)
• Hardware-based approaches to privacy-preserving machine learning
• Hardware-based approaches to learning on encrypted data
• Practical performance evaluations of different approaches to privacy-preserving machine learning
• Private multi-party machine learning
• Distributed privacy-preserving algorithms for private machine learning
• Decentralized protocols for learning on encrypted data
• Secure big data analytics
• Privacy-preserving big data analytics


Keywords: machine learning, privacy, deep learning, neural networks, data mining, security, data analytics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Machine learning algorithms, such as deep learning algorithms, have attracted attention as a breakthrough in computer vision, speech recognition, and other areas. However, machine learning algorithms require access to raw data which is often privacy sensitive. On the other hand, machine learning systems can be fragile and easily fooled by attacks that are effective across different architectures and applications. A novel combination of techniques is needed to enable secure privacy-preserving machine learning. Different approaches based on differential privacy-like techniques, homomorphic encryption where operations are performed on encrypted data, and techniques from the field of secure multi-party computation are being developed to deal with this problem.

The aim of this Article Collection is to bring together world-leading research on issues related to security and privacy of machine learning and data analytics and serve as a forum to unify different perspectives on this problem and explore the relative merits of each approach.

We invite submissions of novel research from academic researchers, practitioners and industry, both theory and application-oriented, on secure privacy-preserving machine learning and data analytics. The topics include but not limited to:

• Secure multi-party computation techniques for machine learning
• Homomorphic encryption techniques for machine learning
• Differential privacy techniques for machine learning
• Machine learning on encrypted data
• Adversarial machine learning (attacks and defenses)
• Hardware-based approaches to privacy-preserving machine learning
• Hardware-based approaches to learning on encrypted data
• Practical performance evaluations of different approaches to privacy-preserving machine learning
• Private multi-party machine learning
• Distributed privacy-preserving algorithms for private machine learning
• Decentralized protocols for learning on encrypted data
• Secure big data analytics
• Privacy-preserving big data analytics


Keywords: machine learning, privacy, deep learning, neural networks, data mining, security, data analytics


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

07 February 2019 Manuscript
31 May 2019 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

07 February 2019 Manuscript
31 May 2019 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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