Privacy preserving decision tree learning over multiple parties

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Privacy-preserving Transfer Learning for Knowledge Sharing (2018, background): “…privacy while preserving the performance of machine learning, including logistic regression (Chaudhuri, Monteleoni, and Sarwate 2011), tree models (Emekc¸i et al. 2007; Jagannathan, Pillaipakkamnatt, and Wright 2009; Fong and Weber-Jahnke 2012), and deep neural networks (Shokri and Shmatikov…”
Security and Privacy in the Internet of Things (2017, background): “Most existing work falls into this category, which either use cryptographic techniques [107, 143] or privacy-preserving data mining techniques such as value distortion [26], randomized response [61] and partitioned data [144, 63].”
InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy (2018, methodology): “[26] implemented a multi-party ID3 algorithm by secret sharing to utilize each participant’s information gain.”
Distributed Data Mining with Differential Privacy (2011, methodology): “[4] used secure sum primitive to aggregate the individual counts and present only the aggregate count to the miner.”