Publishing decision trees can provide enormous benefits to the society. Meanwhile, it is widely believed that publishing decision trees can pose a potential risk to privacy. However, there is not much investigation on the privacy consequence of publishing decision trees. To understand this problem, we need to quantitatively measure privacy risk. Based on the well-established maximum entropy the...

A decision tree is a well-known machine learning technique. Recently their popularity has increased due to the powerful Gradient Boosting ensemble method that allows gradually increasing accuracy at cost of executing large number trees. In this paper we present an accelerator designed optimize execution these trees while reducing energy consumption. We have implemented it in FPGA for embedded s...

Decision trees (DTs) epitomize the ideal of interpretability machine learning (ML) models. The decision motivates explainability approaches by so-called intrinsic interpretability, and it is at core recent proposals for applying interpretable ML models in high-risk applications. belief DT justified fact that explanations predictions are generally expected to be succinct. Indeed, case DTs, corre...