InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy

By Lingchen Zhao, Lihao Ni, Shengshan Hu, Yanjiao Chen, Pan Zhou, Fu Xiao, Libing Wu. 2018

93 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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. Yang Liu
2. Bingsheng He
3. Qinbin Li
4. Qiang Yang
5. Zeyi Wen
6. Tianjian Chen
7. Tao Fan
8. Brij Bhooshan Gupta

Citations in the corpus

• SecureBoost: A Lossless Federated Learning Framework (2019, background): “Following the same idea, several attempts have been made to reinvent different machine learning models to the federated setting, including decision tree [20], [21], linear/logistic regression [22], [23], [24] and neural network [25], [26].”
• Cloud-based Federated Boosting for Mobile Crowdsensing (2020, methodology): “ning framework for the universal face recognition system by exploiting additive secret sharing. Considering the wide applications of gradient boosting decision tree (GDBT) in data mining, Zhao et al. [30] utilized the differential privacy technology to implement two novel privacy-preserving schemes ”
• A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, unknown): “In Table III, we explore such studies (e.g., [39]) as “Client-centric” in the “Decision-making” column.”
• Practical Federated Gradient Boosting Decision Trees (2019, methodology): “A recent study adopted differential privacy to aggregate distributed regression trees (Zhao et al. 2018).”