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首页> 外文期刊>International Journal of High Performance Computing and Networking >Outsourcing privacy-preserving ID3 decision tree over horizontally partitioned data for multiple parties
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Outsourcing privacy-preserving ID3 decision tree over horizontally partitioned data for multiple parties

机译：在多方的水平分区数据中外包隐私保留ID3决策树

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摘要

Today, many small and medium-sized companies want to share data for data mining; however, privacy and security concerns restrict such data sharing. Privacy-preserving data mining has emerged as a solution to this problem. Nevertheless, the traditional cryptographic solutions are too inefficient and infeasible to allow the large-scale analytics needed for big data. In this paper, we focus on the outsourcing of privacy-preserving ID3 decision trees over horizontally partitioned data for multiple parties. We outsource most of the protocol computation to the cloud and propose the OPPWAP to protect users' data privacy. By this method, each party can have the correct results calculated with data from other parties and the cloud, and each party's data are kept private from other parties and the cloud. Our findings indicate that an increase in the number of participating parties results in a slight computing cost increase on the user's side.
机译：今天，许多中小型公司希望分享数据挖掘的数据; 但是，隐私和安全问题限制了此类数据共享。 保留隐私数据挖掘已成为此问题的解决方案。 然而，传统的加密解决方案过于低效，无法允许大数据所需的大规模分析。 在本文中，我们专注于将隐私保留ID3决策树的外包在多方的水平分区数据中。 我们将大多数协议计算外包给云并提出了对抗保护用户的数据隐私。 通过此方法，每个方可以具有从其他方和云数据的数据计算的正确结果，并且每个方的数据都保留了其他方和云的私人。 我们的调查结果表明，参与缔约方数量的增加导致用户侧的略微计算成本增加。

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