Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially involves frequent dataset splitting according to the splitting criterion (e.g. Gini impurity). However, existing multi-party training frameworks for decision trees demonstrate communication inefficiency due to the following issues: (1) They suffer from huge communication overhead in securely splitting a dataset with continuous attributes. (2) They suffer from huge communication overhead due to performing almost all the computations on a large ring to accommodate the secure computations for the splitting criterion. In this paper, we are motivated to present an efficient three-party training framework, namely Ents, for decision trees by communication optimization. For the first issue, we present a series of training protocols based on the secure radix sort protocols to efficiently and securely split a dataset with continuous attributes. For the second issue, we propose an efficient share conversion protocol to convert shares between a small ring and a large ring to reduce the communication overhead incurred by performing almost all the computations on a large ring. Experimental results from eight widely used datasets show that Ents outperforms state-of-the-art frameworks by $5.5\times \sim 9.3\times$ in communication sizes and $3.9\times \sim 5.3\times$ in communication rounds. In terms of training time, Ents yields an improvement of $3.5\times \sim 6.7\times$. To demonstrate its practicality, Ents requires less than three hours to securely train a decision tree on a widely used real-world dataset (Skin Segmentation) with more than 245,000 samples in the WAN setting.

Vector 2.0 may only be the size of a hamster, but the little robot has a big personality. This little tracked AI companion is now shipping globally to 65 countries. Despite myriad supply chain difficulties, Digital Dream Labs has kept its target promise to start shipping Vector 2.0, after passing regional certifications. US and Canadian pre-orders are now shipping. UK and EU distribution has begun, and the remaining regions will soon follow.

Editor's Note: This article was updated on December 27, 2019 at 11 AM EST. The Robot Report spoke to Digital Dream Labs Founder H. Jacob Hanchar and added new information about relaunching Anki products and a potential subscription model and open-source Vector 2.0. Anki's robots might be making a comeback, after all. Digital Dream Labs, a Pittsburgh-based edtech startup, acquired all of Anki's assets – patents, trademarks, data, social media, and domain. It has hidden the Anki portfolio page from its website, but it remains active. Digital Dream Labs was founded in February 2015 by H. Jacob Hanchar, who has an MBA from Carnegie Mellon University.