PRIVACY-PRESERVING REGRESSION ALGORITHM BASED ON PROXIMAL SUPPORT VECTOR MACHINE

With the advent of the big data era, the privacy-preserving data mining is gaining a significant importance. The present study envisaged the development of a privacy-preserving data mining, based on the proximal support vector regression (PPSVR). The algorithm was based on a distributed system, and it was shown that the global kernel could be calculated by the local kernel. In order to protect the data privacy, the stochastic noise was added to the original data on each data set, and each participant had to provide only the disturbed local kernel. Furthermore, simulation experiments were performed on the algorithm and the results indicated that the accuracy of the PPSVR was almost equal to the proximal support vector regression algorithm (PSVR). The algorithm takes advantage of the speed of the PSVR, and the experimental validation showed that the speed of the PPSVR was faster than the support vector regression (SVR).