Anonymous Publication of Sensitive Transactional Data

Abstract—Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as k-anonymity and -diversity, while minimizing the information loss incurred in the anonymizing process (i.e., maximize data utility). Existing techniques work well for fixedschema data, with low dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transactional data (or basket data), which involve hundreds or even thousands of dimensions, rendering existing methods unusable. We propose two categories of novel anonymization methods for sparse high-dimensional data. The first category is based on approximate nearest-neighbor (NN) search in high-dimensional spaces, which is efficiently performed through locality-sensitive hashing (LSH). In the second category, we propose two data transformations that capture the correlation in the underlying data: (i) reduction to a band ma...

• Type: Journal
• Year: 2011
• Where: TKDE
• Authors: Gabriel Ghinita, Panos Kalnis, Yufei Tao
• Keywords: Band Matrix, Data Transformation, Data Utility, TKDE 2011