Practical Inference Control for Data Cubes
The fundamental problem for inference control in data cubes is how to efficiently calculate the lower and upper bounds for each cell value given the aggregations of cell values over multiple dimensions. In this paper, we provide the first practical solution for estimating exact bounds in two-dimensi...
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sg-smu-ink.sis_research-15962015-12-20T08:00:37Z Practical Inference Control for Data Cubes LI, Yingjiu LU, Haibing DENG, Robert H. The fundamental problem for inference control in data cubes is how to efficiently calculate the lower and upper bounds for each cell value given the aggregations of cell values over multiple dimensions. In this paper, we provide the first practical solution for estimating exact bounds in two-dimensional irregular data cubes (i.e., data cubes in which certain cell values are known to a snooper). Our results imply that the exact bounds cannot be obtained by a direct application of the Fréchet bounds in some cases. We then propose a new approach to improve the classic Fréchet bounds for any high-dimensional data cube in the most general case. The proposed approach improves upon the Fréchet bounds in the sense that it gives bounds that are at least as tight as those computed by Fréchet, yet is simpler in terms of time complexity. Based on our solutions to the fundamental problem, we discuss two security applications, privacy protection of released data and fine-grained access control and auditing. 2006-03-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/597 info:doi/10.1109/SP.2006.31 http://dx.doi.org/10.1109/SP.2006.31 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Information Security |
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Information Security LI, Yingjiu LU, Haibing DENG, Robert H. Practical Inference Control for Data Cubes |
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The fundamental problem for inference control in data cubes is how to efficiently calculate the lower and upper bounds for each cell value given the aggregations of cell values over multiple dimensions. In this paper, we provide the first practical solution for estimating exact bounds in two-dimensional irregular data cubes (i.e., data cubes in which certain cell values are known to a snooper). Our results imply that the exact bounds cannot be obtained by a direct application of the Fréchet bounds in some cases. We then propose a new approach to improve the classic Fréchet bounds for any high-dimensional data cube in the most general case. The proposed approach improves upon the Fréchet bounds in the sense that it gives bounds that are at least as tight as those computed by Fréchet, yet is simpler in terms of time complexity. Based on our solutions to the fundamental problem, we discuss two security applications, privacy protection of released data and fine-grained access control and auditing. |
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LI, Yingjiu LU, Haibing DENG, Robert H. |
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LI, Yingjiu LU, Haibing DENG, Robert H. |
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LI, Yingjiu |
title |
Practical Inference Control for Data Cubes |
title_short |
Practical Inference Control for Data Cubes |
title_full |
Practical Inference Control for Data Cubes |
title_fullStr |
Practical Inference Control for Data Cubes |
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Practical Inference Control for Data Cubes |
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practical inference control for data cubes |
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Institutional Knowledge at Singapore Management University |
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2006 |
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https://ink.library.smu.edu.sg/sis_research/597 http://dx.doi.org/10.1109/SP.2006.31 |
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