Slicing A New Approach to Privacy Preserving Data Publishing

Abstract:

Several anonymization techniques, such as generalization and bucketization, 

have been designed for privacy preserving microdata publishing. Recent 

work has shown that generalization loses considerable amount of information, 

especially for high-dimensional data. Bucketization, on the other hand, does 

not prevent membership disclosure and does not apply for data that do not 

have a clear separation between quasi-identifying attributes and sensitive 

attributes. In this paper, we present a novel technique called slicing, which 

partitions the data both horizontally and vertically. We show that slicing 

preserves better data utility than generalization and can be used for 

membership disclosure protection. Another important advantage of slicing is 

that it can handle high-dimensional data. We show how slicing can be used 

for attribute disclosure protection and develop an efficient algorithm for 

computing the sliced data that obey the ℓ-diversity requirement. Our workload 

experiments confirm that slicing preserves better utility than generalization 

and is more effective than bucketization in workloads involving the sensitive 

attribute. Our experiments also demonstrate that slicing can be used to 

prevent membership disclosure.

Algorithm Used:

Slicing Algorithms

 

Advantage of slicing is its ability to handle high-dimensional data. By 

partitioning attributes into columns, slicing reduces the dimensionality of the 

data. Each column of the table can be viewed as a sub-table with a lower 

dimensionality. Slicing is also different from the approach of publishing 

multiple independent sub-tables in that these sub-tables are linked by the 

buckets in slicing.