The Experts below are selected from a list of 99612 Experts worldwide ranked by ideXlab platform
Shigang Chen - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving Multivariate Statistical Analysis linear regression and classification
SIAM International Conference on Data Mining, 2004Co-Authors: Yunghsiang S Han, Shigang ChenAbstract:Multivariate Statistical Analysis is an important data Analysis technique that has found applications in various areas. In this paper, we study some Multivariate Statistical Analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the Statistical Analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current Statistical Analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party Multivariate Statistical Analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
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SDM - Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
2004Co-Authors: Yunghsiang S Han, Shigang ChenAbstract:Multivariate Statistical Analysis is an important data Analysis technique that has found applications in various areas. In this paper, we study some Multivariate Statistical Analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the Statistical Analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current Statistical Analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party Multivariate Statistical Analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
Yunghsiang S Han - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving Multivariate Statistical Analysis linear regression and classification
SIAM International Conference on Data Mining, 2004Co-Authors: Yunghsiang S Han, Shigang ChenAbstract:Multivariate Statistical Analysis is an important data Analysis technique that has found applications in various areas. In this paper, we study some Multivariate Statistical Analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the Statistical Analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current Statistical Analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party Multivariate Statistical Analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
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SDM - Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
2004Co-Authors: Yunghsiang S Han, Shigang ChenAbstract:Multivariate Statistical Analysis is an important data Analysis technique that has found applications in various areas. In this paper, we study some Multivariate Statistical Analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the Statistical Analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current Statistical Analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party Multivariate Statistical Analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
Chunnian Liu - One of the best experts on this subject based on the ideXlab platform.
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Combining Multiple Email Filters Based on Multivariate Statistical Analysis
Lecture Notes in Computer Science, 2006Co-Authors: Ning Zhong, Chunnian LiuAbstract:In this paper, we investigate how to combine multiple e-mail filters based on Multivariate Statistical Analysis for providing a barrier to spam, which is stronger than a single filter alone. Three evaluation criteria are suggested for cost-sensitive filters, and their rationality is discussed. Furthermore, a principle that minimizes the error cost is described to avoid filtering an e-mail of Legitimate into Spam. Comparing with other major methods, the experimental results show that our method of combining multiple filters has preferable performance when appropriate running parameters are adopted.
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ISMIS - Combining multiple email filters based on Multivariate Statistical Analysis
Lecture Notes in Computer Science, 2006Co-Authors: Ning Zhong, Chunnian LiuAbstract:In this paper, we investigate how to combine multiple e-mail filters based on Multivariate Statistical Analysis for providing a barrier to spam, which is stronger than a single filter alone. Three evaluation criteria are suggested for cost-sensitive filters, and their rationality is discussed. Furthermore, a principle that minimizes the error cost is described to avoid filtering an e-mail of “Legitimate” into “Spam”. Comparing with other major methods, the experimental results show that our method of combining multiple filters has preferable performance when appropriate running parameters are adopted.
Wayne B Bequette - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Statistical Analysis to detect insulin infusion set failure
Proceedings of the 2011 American Control Conference, 2011Co-Authors: Ruben Rojas, Winston Garcia-gabin, Wayne B BequetteAbstract:Multivariate Statistical Analysis techniques are applied to insulin infusion set failure detection (IISF), a challenging problem faced by individuals with type 1 diabetes that are on continuous insulin infusion pump therapy. Bivariate classification (BC), principal component Analysis (PCA), and a combined approach were applied to simulated glucose concentrations for 10 patients, based on a nonlinear physiological model of insulin and glucose dynamics. The PCA algorithm had fewer false alarms than BC, while detecting most drifting (ramp) infusion set failures before complete failure occurred.
Ning Zhong - One of the best experts on this subject based on the ideXlab platform.
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Combining Multiple Email Filters Based on Multivariate Statistical Analysis
Lecture Notes in Computer Science, 2006Co-Authors: Ning Zhong, Chunnian LiuAbstract:In this paper, we investigate how to combine multiple e-mail filters based on Multivariate Statistical Analysis for providing a barrier to spam, which is stronger than a single filter alone. Three evaluation criteria are suggested for cost-sensitive filters, and their rationality is discussed. Furthermore, a principle that minimizes the error cost is described to avoid filtering an e-mail of Legitimate into Spam. Comparing with other major methods, the experimental results show that our method of combining multiple filters has preferable performance when appropriate running parameters are adopted.
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ISMIS - Combining multiple email filters based on Multivariate Statistical Analysis
Lecture Notes in Computer Science, 2006Co-Authors: Ning Zhong, Chunnian LiuAbstract:In this paper, we investigate how to combine multiple e-mail filters based on Multivariate Statistical Analysis for providing a barrier to spam, which is stronger than a single filter alone. Three evaluation criteria are suggested for cost-sensitive filters, and their rationality is discussed. Furthermore, a principle that minimizes the error cost is described to avoid filtering an e-mail of “Legitimate” into “Spam”. Comparing with other major methods, the experimental results show that our method of combining multiple filters has preferable performance when appropriate running parameters are adopted.
