Privacy preserving data mining using particle swarm optimisation trained auto-associative neural network: an application to bankruptcy prediction in banks
N.A. Paramjeet, Vadlamani Ravi, Nekuri Naveen, Chillarige Raghavendra Rao · International Journal of Data Mining Modelling and Management · 2012
While data mining made inroads into the diverse areas it also entails violation of individual privacy leading to legal complications in areas like medicine and finance as consequently, privacy preserving data mining (PPDM) emerged as a new area. To achieve an equitable solution to this problem, data owners must not only preserve privacy and but also guarantee valid data mining results. This paper proposes a novel particle swarm optimisation (PSO) trained auto associative neural network (PSOAANN) for privacy preservation. Then, decision tree and logistic regression are invoked for data mining purpose, leading to PSOAANN + DT and PSOAANN + LR hybrids. The efficacy of hybrids is tested on five benchmark and four bankruptcy datasets. The results are compared with those of Ramu and Ravi (2009) and others. It was observed that the proposed hybrids yielded better or comparable results. We conclude that PSOAANN can be used as viable approach for privacy preservation.