Privacy-preserving outsourced classification in cloud computing

By Ping Li, Jin Li, Zheng-an Huang, Chong-zhi Gao, Wenbin Chen, Kai Chen. Cluster Computing, 2017

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/efb9a7fcd989093df2c249d9ab1f13d8eb3db643

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. Amir Salman Avestimehr
2. Basak Guler
3. Jinhyun So
4. Lichao Sun
5. Weiqi Luo
6. Jian Weng
7. Payman Mohassel
8. Ming Li

In Information Systems

1. Jinhyun So
2. Weiqi Luo
3. Jian Weng
4. Payman Mohassel
5. Basak Guler
6. Ming Li
7. Lichao Sun
8. Jiasi Weng

Citations in the corpus

• CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019, methodology): “One is based on Homomorphic Encryption (HE) (Gentry & Boneh, 2009) which allows for computation to be performed over encrypted data, and has been used to enable privacy-preserving machine learning solutions (GiladBachrach et al., 2016; Hesamifard et al., 2017; Graepel et al., 2012; Yuan & Yu, 2014; ”
• DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (2019, background): “been around in the past few years [8], [9], [10], [11], [12], [13], [14], [15], [16].”
• Significant Permission Identification for Machine-Learning-Based Android Malware Detection (2018, background): “the privacy concerns then become a potential problem [19]– [21].”
• A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020, background): “The first one is Homomorphic Encryption (HE) [16], which enables computations on encrypted data, and has been applied to privacy-preserving machine learning [17], [18], [19], [20], [21], [22], [23], [24].”