A reversible data transform algorithm using integer transform for privacy-preserving data mining

Abstract

In the cloud computing environment, since data owners worry about private information in their data being disclosed without permission, they try to retain the knowledge within the data, while applying privacy-preserving techniques to the data. In the past, a data perturbation approach was commonly used to modify the original data content, but it also results in data distortion, and hence leads to significant loss of knowledge within the data. To solve this problem, this study introduced the concept of reversible integer transformation in the image processing domain and developed a Reversible Data Transform (RDT) algorithm that can disrupt and restore data. In the RDT algorithm, using an adjustable weighting mechanism, the degree of data perturbation was adjusted to increase the flexibility of privacy-preserving. In addition, it allows the data to be embedded with a watermark, in order to identify whether the perturbed data has been tampered with. Experimental results show that, compared with the existing algorithms, RDT has better knowledge reservation and is better in terms of effectively reducing information loss and privacy disclosure risk. In addition, it has a high watermark payload.