The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Zequn Wu - One of the best experts on this subject based on the ideXlab platform.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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ICDE - Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying e-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies e-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering ICDE 2016, 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
Xiang Cheng - One of the best experts on this subject based on the ideXlab platform.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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ICDE - Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying e-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies e-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering ICDE 2016, 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
Sen Su - One of the best experts on this subject based on the ideXlab platform.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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ICDE - Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying e-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies e-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering ICDE 2016, 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
Rui Chen - One of the best experts on this subject based on the ideXlab platform.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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ICDE - Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying e-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies e-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering ICDE 2016, 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
Peng Tang - One of the best experts on this subject based on the ideXlab platform.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Multi-Party High-Dimensional Data Publishing Under Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 2020Co-Authors: Xiang Cheng, Peng Tang, Sen Su, Rui Chen, Zequn WuAbstract:In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn N. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update N. By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies ε-differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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ICDE - Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying e-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies e-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.
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Differentially private multi-party high-dimensional data publishing
2016 IEEE 32nd International Conference on Data Engineering ICDE 2016, 2016Co-Authors: Sen Su, Peng Tang, Xiang Cheng, Rui Chen, Zequn WuAbstract:In this paper, we study the novel problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the involved parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying ε-differential privacy for any local dataset. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) solution. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network ℕ that best fits the integrated dataset D in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the statistical results provided by previous parties as their prior knowledge to direct how to learn ℕ. The core of DP-SUBN is the construction of the Search Frontier, which can be seen as a priori knowledge to guide the parties to update ℕ. To improve the fitness of ℕ and reduce the communication cost, we introduce a correlation-aware Search Frontier construction (CSFC) approach, where attribute pairs with strong correlations are used to construct the Search Frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first propose a non-overlapping covering design (NOCD) method, and then introduce a dynamic programming method to find the optimal parameters used in NOCD to ensure that the injected noise is minimum. Through formal privacy analysis, we show that DP-SUBN satisfies ε-differential privacy for any local dataset. Extensive experiments on a real dataset demonstrate that DP-SUBN offers desirable data utility with low communication cost.