A survey on federated learning systems: Vision, hype and reality for data privacy and protection
As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …
been a hot research topic in enabling the collaborative training of machine learning models …
Decision tree classification with differential privacy: A survey
S Fletcher, MZ Islam - ACM Computing Surveys (CSUR), 2019 - dl.acm.org
Data mining information about people is becoming increasingly important in the data-driven
society of the 21st century. Unfortunately, sometimes there are real-world considerations that …
society of the 21st century. Unfortunately, sometimes there are real-world considerations that …
Machine unlearning for random forests
Responding to user data deletion requests, removing noisy examples, or deleting corrupted
training data are just a few reasons for wanting to delete instances from a machine learning …
training data are just a few reasons for wanting to delete instances from a machine learning …
Differentially private data publishing and analysis: A survey
Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …
explored in recent decades. This survey provides a comprehensive and structured overview …
Multinomial random forest
Despite the impressive performance of random forests (RF), its theoretical properties have
not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed …
not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed …
A survey on differentially private machine learning
M Gong, Y Xie, K Pan, K Feng… - IEEE computational …, 2020 - ieeexplore.ieee.org
Recent years have witnessed remarkable successes of machine learning in various
applications. However, machine learning models suffer from a potential risk of leaking …
applications. However, machine learning models suffer from a potential risk of leaking …
Federated boosted decision trees with differential privacy
There is great demand for scalable, secure, and efficient privacy-preserving machine
learning models that can be trained over distributed data. While deep learning models …
learning models that can be trained over distributed data. While deep learning models …
Inprivate digging: Enabling tree-based distributed data mining with differential privacy
Data mining has heralded the major breakthrough in data analysis, serving as a “super
cruncher” to discover hidden information and valuable knowledge in big data systems. For …
cruncher” to discover hidden information and valuable knowledge in big data systems. For …
Revfrf: Enabling cross-domain random forest training with revocable federated learning
Random forest is one of the most heated machine learning tools in a wide range of industrial
scenarios. Recently, federated learning enables efficient distributed machine learning …
scenarios. Recently, federated learning enables efficient distributed machine learning …