Abstract
Aiming at solving the problem of deducing sensitive information leakage after big data aggregation, this paper proposes an information sensitivity inference method for big data aggregation based on granular analysis. Firstly, data conceptual objects are generated based on data attributes and attribute values, and data granular sets are formed. By calculating the quality and gravity of data granules, the data granules are analyzed and classified into equivalence classes. Then, subdividing the equivalence classes according to the decision data granules, the data granules sets in positive and critical domains of decision data granules are established respectively, and the data granular characteristic matrix and eigenvalue matrix are constructed. By the approximate data granules dynamic updating algorithm, the dynamic clustering of data granules is realized when the values of objects, attributes and attributes increase dynamically. Finally, a similar granules cloud is established. Based on the attribute fuzzy set probability measure and the contribution of the data granule to the sensitive granules cloud, the possibility of deducing sensitive information by similar data is inferred. What’s more, the performance of data granular clustering algorithm and dynamic update algorithm, and the accuracy of the inference algorithm are verified. This method is helpful for the formulation of large data access control strategy and the analysis of similar data, as well as reduce the risk of information leakage.
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Acknowledgements
This work is supported by the National Natural Science Foundations of China (grant No. 61502531 and No. 61702550) and the National Key Research and Development Plan (grant No. 2018YFB0803603 and No. 2016YFB0501901).
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Cao, L., Lu, X., Gao, Z., Zhu, Z. (2019). An Information Sensitivity Inference Method for Big Data Aggregation Based on Granular Analysis. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_18
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DOI: https://doi.org/10.1007/978-981-15-1899-7_18
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