Privacy-Preserving Machine Learning Algorithms for Big Data Systems

By Kaihe Xu, Hao Yue, Linke Guo, Yuanxiong Guo, Yuguang Fang. 2015

94 citations recorded by the source corpus.

Subfields: Artificial Intelligence, ML Systems & Hardware, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hayat Khaloufi
2. Prateek Saxena
3. Abderrahim Beni Hssane
4. Shiqi Shen
5. Shruti Tople
6. Karim Abouelmehdi
7. Chris J. M. Yoon
8. Clement Fung

In ML Systems & Hardware

1. Prateek Saxena
2. Shruti Tople

In Privacy & Security

1. Hayat Khaloufi
2. Prateek Saxena
3. Abderrahim Beni Hssane
4. Shruti Tople
5. Karim Abouelmehdi
6. Chris J. M. Yoon
7. Clement Fung
8. Ivan Beschastnikh

Citations in the corpus

• Auror: defending against poisoning attacks in collaborative deep learning systems (2016, background): “Various researches have shown the benefits of collaborative learning in the area of machine learning algorithms [28, 45].”
• Big healthcare data: preserving security and privacy (2018, methodology): “In addition, paper [72] suggested a novel framework to achieve privacy-preserving machine learning and paper [73] proposed methodology provides data confidentiality and secure data sharing.”
• Big data privacy: a technological perspective and review (2016, unknown): “Big data privacy preserving in data processing Big data processing paradigm categorizes systems into batch, stream, graph, and machine learning processing [27, 28].”