Random locally linear embedding on encrypted case based reasoning method

Case based reasoning (CBR) is very important task in data mining, but privacy information will be disclosed easily in CBR. This paper presents random locally linear embedding (LLE) on encrypted case based reasoning method. In order to be ensure the security of the CBR, the parameters nearest neighbor number k and embedded space dimension d of LLE algorithm are selected randomly. Further we embed the sensitive attribution into random dimension space using random LLE, thus the sensitive attributes are encrypted and protected. Because the transformed space dimension d and nearest neighbor number k are both random, this algorithm is very secure. In addition, LLE can keep the inherent shape of dataset, so the precision change of CBR after encryption can be controlled in a small scope. The experiment show that if we select appropriate parameters, then nearest neighbors of every case may be almost consistent. The present algorithm can guarantee that the security and the precision both achieve the requirements.