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Recently, privacy protection attracts more and more research efforts especially for social network data. In the literature, most of existing approaches assume that social data are independent to each other which is not the case in the real world applications. In this paper, we are the first attempt to investigate the privacy protection issue when social data are correlated. We model the correlation...
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...
With the next-generation Web aiming to further facilitate data/information sharing and aggregation, providing data privacy protection support in an open networked environments becomes increasingly important. Learning-from abstraction is a recently proposed distributed data mining approach which first abstracts data at local sources using the agglomerative hierarchical clustering (AGH) algorithm and...
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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