{"id":1064340,"date":"2024-08-02T00:29:34","date_gmt":"2024-08-02T07:29:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1064340"},"modified":"2024-08-15T09:06:38","modified_gmt":"2024-08-15T16:06:38","slug":"securely-training-decision-trees-efficiently","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/securely-training-decision-trees-efficiently\/","title":{"rendered":"Securely Training Decision Trees Efficiently"},"content":{"rendered":"

Decision trees are an important class of supervised learning algo<\/span>rithms. When multiple entities contribute data to train a decision <\/span>tree (e.g. for fraud detection in the financial sector), data privacy <\/span>concerns necessitate the use of a privacy-enhancing technology <\/span>such as secure multi-party computation (MPC) in order to secure the <\/span>underlying training data. Prior state-of-the-art (Hamada<\/span> et al.<\/span>) <\/span>construct an MPC protocol for decision tree training with a commu<\/span>nication of<\/span> O (<\/span>\u210e\ud835\udc5a\ud835\udc41<\/span> log<\/span> \ud835\udc41<\/span> )<\/span>, when building a decision tree of height <\/span>\u210e<\/span> for a training dataset of<\/span> \ud835\udc41<\/span> samples, each having<\/span> \ud835\udc5a<\/span> attributes.\u00a0<\/span><\/p>\n

In this work, we significantly reduce the communication com<\/span>plexity of secure decision tree training. We construct a protocol <\/span>with communication complexity<\/span> O (<\/span>\ud835\udc5a\ud835\udc41<\/span> log<\/span> \ud835\udc41<\/span> +<\/span> \u210e\ud835\udc5a\ud835\udc41<\/span> +<\/span> \u210e\ud835\udc41<\/span> log<\/span> \ud835\udc41<\/span> )<\/span>, <\/span>thereby achieving an improvement of<\/span> \u2248<\/span> min<\/span>(<\/span>\u210e, \ud835\udc5a,<\/span> log<\/span> \ud835\udc41<\/span> )<\/span> over Hamada et al<\/span>. <\/span>At the core of our technique is an improved protocol to regroup <\/span>sorted private elements further into additional groups (according <\/span>to a flag vector) while maintaining their relative ordering. We im<\/span>plement our protocol in the MP-SPDZ framework<\/span>\u00a0and show <\/span>that it requires<\/span> 10<\/span>\u00d7<\/span> lesser communication and is<\/span> 9<\/span>\u00d7<\/span> faster than Hamada et al.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada 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