A privacy-preserving classification method based on singular value decomposition

Abstract EN

With the development of data mining technologies, privacy protection has become a challenge for data mining applications in many fields.

To solve this problem, many privacy-preserving data mining methods have been proposed.

One important type of such methods is based on Singular Value Decomposition (SVD).

The SVD-based method provides perturbed data instead of original data, and users extract original data patterns from perturbed data.

The original SVD-based method perturbs all samples to the same degree.

However, in reality, different users have different requirements for privacy protection, and different samples are not equally important for data mining.

Thus, it is better to perturb different samples to different degrees.

This paper improves the SVD-based data perturbation method so that it can perturb different samples to different degrees.

In addition, we propose a new privacy-preserving classification mining method using our improved SVD-based perturbation method and sample selection.

The experimental results indicate that compared with the original SVD-based method, this new proposed method is more efficient in balancing data privacy and data utility.