The Experts below are selected from a list of 28464 Experts worldwide ranked by ideXlab platform
Zhengan Huang - One of the best experts on this subject based on the ideXlab platform.
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outsourced privacy preserving c4 5 Decision Tree Algorithm over horizontally and vertically partitioned dataset among multiple parties
Cluster Computing, 2019Co-Authors: Zoe Lin Jiang, Xuan Wang, Lin Yao, Siuming Yiu, Zhengan HuangAbstract:Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 Algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 Decision Tree Algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the Algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
Zoe Lin Jiang - One of the best experts on this subject based on the ideXlab platform.
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outsourced privacy preserving c4 5 Decision Tree Algorithm over horizontally and vertically partitioned dataset among multiple parties
Cluster Computing, 2019Co-Authors: Zoe Lin Jiang, Xuan Wang, Lin Yao, Siuming Yiu, Zhengan HuangAbstract:Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 Algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 Decision Tree Algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the Algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
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outsourcing privacy preserving id3 Decision Tree Algorithm over encrypted data sets for two parties
Trust Security And Privacy In Computing And Communications, 2017Co-Authors: Zoe Lin Jiang, Xuan Wang, S M Yiu, Peng ZhangAbstract:ID3 Decision Tree data mining is a popular and widely studied data analysis technique for a range of applications. In this paper, we focus on the privacy-preserving ID3 Decision Tree Algorithm on horizontally partitioned datasets. In such a scenario, data owners wish to learn the Decision Tree result from a collective data set but disclose minimal information about their own sensitive data. In this paper, we consider a scenario in which multiple parties with weak computational power need to run an ID3 Algorithm on their databases jointly while simultaneously outsourcing most of the computation of the protocol and databases to the cloud. In such a scenario, each party can have the correct result calculated on the data from all the parties with most of the computation outsourced to the cloud. Concerning privacy, the data owned by each party should be kept confidential from both the other parties and the cloud. To ensure data privacy, we modify the Secure Equivalent Testing Protocol (SET) and design the Outsourced Secure Shared xlnx Protocol (OSSx ln x) and other sub-protocols. We then propose a cloud-aided ID3 solution based on these protocols, which is used to build an outsourced privacy-preserving ID3 data mining solution.
Xuan Wang - One of the best experts on this subject based on the ideXlab platform.
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outsourced privacy preserving c4 5 Decision Tree Algorithm over horizontally and vertically partitioned dataset among multiple parties
Cluster Computing, 2019Co-Authors: Zoe Lin Jiang, Xuan Wang, Lin Yao, Siuming Yiu, Zhengan HuangAbstract:Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 Algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 Decision Tree Algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the Algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
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outsourcing privacy preserving id3 Decision Tree Algorithm over encrypted data sets for two parties
Trust Security And Privacy In Computing And Communications, 2017Co-Authors: Zoe Lin Jiang, Xuan Wang, S M Yiu, Peng ZhangAbstract:ID3 Decision Tree data mining is a popular and widely studied data analysis technique for a range of applications. In this paper, we focus on the privacy-preserving ID3 Decision Tree Algorithm on horizontally partitioned datasets. In such a scenario, data owners wish to learn the Decision Tree result from a collective data set but disclose minimal information about their own sensitive data. In this paper, we consider a scenario in which multiple parties with weak computational power need to run an ID3 Algorithm on their databases jointly while simultaneously outsourcing most of the computation of the protocol and databases to the cloud. In such a scenario, each party can have the correct result calculated on the data from all the parties with most of the computation outsourced to the cloud. Concerning privacy, the data owned by each party should be kept confidential from both the other parties and the cloud. To ensure data privacy, we modify the Secure Equivalent Testing Protocol (SET) and design the Outsourced Secure Shared xlnx Protocol (OSSx ln x) and other sub-protocols. We then propose a cloud-aided ID3 solution based on these protocols, which is used to build an outsourced privacy-preserving ID3 data mining solution.
Siuming Yiu - One of the best experts on this subject based on the ideXlab platform.
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outsourced privacy preserving c4 5 Decision Tree Algorithm over horizontally and vertically partitioned dataset among multiple parties
Cluster Computing, 2019Co-Authors: Zoe Lin Jiang, Xuan Wang, Lin Yao, Siuming Yiu, Zhengan HuangAbstract:Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 Algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 Decision Tree Algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the Algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
Lin Yao - One of the best experts on this subject based on the ideXlab platform.
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outsourced privacy preserving c4 5 Decision Tree Algorithm over horizontally and vertically partitioned dataset among multiple parties
Cluster Computing, 2019Co-Authors: Zoe Lin Jiang, Xuan Wang, Lin Yao, Siuming Yiu, Zhengan HuangAbstract:Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 Algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 Decision Tree Algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the Algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.
