Fourier Magnitude-Based Privacy-Preserving Clustering on Time-Series Data

作者：Kim Hea Suk; Moon Yang Sae^*

来源：IEICE Transactions on Information and Systems, 2010, E93D(6): 1648-1651.

DOI：10.1587/transinf.E93.D.1648

• time-series data
• clustering
• privacy-preserving
• Fourier magnitude
• distance order

Privacy-preserving clustering (PPC in short) is important in publishing sensitive time-series data. Previous PPC solutions, however, have a problem of not preserving distance orders or incurring privacy breach. To solve this problem, we propose a new PPC approach that exploits Fourier magnitudes of time-series. Our magnitude-based method does not cause privacy breach even though its techniques or related parameters are publicly revealed. Using magnitudes only, however, incurs the distance order problem, and we thus present magnitude selection strategies to preserve as many Euclidean distance orders as possible. Through extensive experiments, we showcase the superiority of our magnitude-based approach.

• 出版日期2010-6