The Experts below are selected from a list of 6987 Experts worldwide ranked by ideXlab platform
Wang Hao - One of the best experts on this subject based on the ideXlab platform.
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2021Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Wang Hao, Liu, Alex X., Hong ChengAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the Requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.Comment: Accepted by KDD'2
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2020Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Ji Xiaoxi, Liu Alex, Wang HaoAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry due to its efficiency, robustness, and interpretability. Meanwhile, with the problem of data isolation and the Requirement of high model performance, building secure and efficient LR model for multi-parties becomes a hot topic for both academia and industry. Existing works mainly employ either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they may suffer potential security risk. In contrast, SS based methods have provable security but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build seCure lArge-scalE SpArse logistic Regression model and thus has the advantages of both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We finally deploy CAESAR into a risk control task and conduct comprehensive experiments to study the efficiency of CAESAR
Chen Chaochao - One of the best experts on this subject based on the ideXlab platform.
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2021Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Wang Hao, Liu, Alex X., Hong ChengAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the Requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.Comment: Accepted by KDD'2
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2020Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Ji Xiaoxi, Liu Alex, Wang HaoAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry due to its efficiency, robustness, and interpretability. Meanwhile, with the problem of data isolation and the Requirement of high model performance, building secure and efficient LR model for multi-parties becomes a hot topic for both academia and industry. Existing works mainly employ either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they may suffer potential security risk. In contrast, SS based methods have provable security but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build seCure lArge-scalE SpArse logistic Regression model and thus has the advantages of both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We finally deploy CAESAR into a risk control task and conduct comprehensive experiments to study the efficiency of CAESAR
Hong Cheng - One of the best experts on this subject based on the ideXlab platform.
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2021Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Wang Hao, Liu, Alex X., Hong ChengAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the Requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.Comment: Accepted by KDD'2
Fang Wenjing - One of the best experts on this subject based on the ideXlab platform.
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2021Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Wang Hao, Liu, Alex X., Hong ChengAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the Requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.Comment: Accepted by KDD'2
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2020Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Ji Xiaoxi, Liu Alex, Wang HaoAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry due to its efficiency, robustness, and interpretability. Meanwhile, with the problem of data isolation and the Requirement of high model performance, building secure and efficient LR model for multi-parties becomes a hot topic for both academia and industry. Existing works mainly employ either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they may suffer potential security risk. In contrast, SS based methods have provable security but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build seCure lArge-scalE SpArse logistic Regression model and thus has the advantages of both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We finally deploy CAESAR into a risk control task and conduct comprehensive experiments to study the efficiency of CAESAR
Wu Xibin - One of the best experts on this subject based on the ideXlab platform.
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2021Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Wang Hao, Liu, Alex X., Hong ChengAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry for its efficiency, robustness, and interpretability. Due to the problem of data isolation and the Requirement of high model performance, many applications in industry call for building a secure and efficient LR model for multiple parties. Most existing work uses either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they incur potential security risks. SS based methods have provable security, but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We have deployed CAESAR in a risk control task and conducted comprehensive experiments. Our experimental results show that CAESAR improves the state-of-the-art model by around 130 times.Comment: Accepted by KDD'2
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When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
2020Co-Authors: Chen Chaochao, Zhou Jun, Li Wang, Wu Xibin, Fang Wenjing, Tan Jin, Wang Lei, Ji Xiaoxi, Liu Alex, Wang HaoAbstract:Logistic Regression (LR) is the most widely used machine learning model in industry due to its efficiency, robustness, and interpretability. Meanwhile, with the problem of data isolation and the Requirement of high model performance, building secure and efficient LR model for multi-parties becomes a hot topic for both academia and industry. Existing works mainly employ either Homomorphic Encryption (HE) or Secret Sharing (SS) to build secure LR. HE based methods can deal with high-dimensional sparse features, but they may suffer potential security risk. In contrast, SS based methods have provable security but they have efficiency issue under high-dimensional sparse features. In this paper, we first present CAESAR, which combines HE and SS to build seCure lArge-scalE SpArse logistic Regression model and thus has the advantages of both efficiency and security. We then present the distributed implementation of CAESAR for Scalability Requirement. We finally deploy CAESAR into a risk control task and conduct comprehensive experiments to study the efficiency of CAESAR
