Privacy Preserving Classification with Meta-learning

Abstract

This proposition is given to protection safeguarding characterization and affiliation rules mining over unified information mutilated with randomisation-based techniques which alter singular esteems indiscriminately to give a normal level of security. It is expected that lone contorted esteems and parameters of a mutilating system are known amid the way toward building a classifier and mining affiliation rules.