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In the era of big data, privacy becomes a challenging issue which already attracts a good number of research efforts. In the literature, most of existing privacy preserving algorithms focus on protecting users' privacy from being disclosed by making the set of designated semi-id features indiscriminate. However, how to automatically determine the appropriate semi-id features from high-dimensional...
In this paper, a modeling method for the data analysis solution is presented and the enterprise level of the data warehousing problem is introduced. The method includes the processes of the requirement elicitation, analysis stage and the initial design of the data warehouse. The extended operation model which can satisfy the need of the data warehouse is also presented in this paper because that it...
Customer data privacy is known to be a factor which makes just-in-time data sharing and mining among enterprises challenging. Learning-from-abstraction is a recently proposed paradigm for privacy preserving distributed data mining where distributed local data sources are protected by probabilistic data abstraction. In this paper, we investigate the use of a normalized negative log likelihood together...
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