Special Issue Article
A New Approach For Collaborative Data Publishing Using Slicing And M-Privacy
We introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering techniques for efficiently checking mprivacy given a set of records. We present a data provider-aware anonymization algorithm with adaptive m- privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency.We introduce a novel data anonymization technique called slicing to improve the current state of the art. Slicing partitions the data set both vertically and horizontally. Vertical partitioning is done by grouping attributes into columns based on the correlations among the attributes. Each column contains a subset of attributes that are highly correlated. Horizontal partitioning is done by grouping tuples into buckets. The basic idea of slicing is to break the association cross columns, but to preserve the association within each column. This reduces the dimensionality of the data and preserves better utility than generalization and bucketization.