kACTUS 2: Privacy Preserving in Classification Tasks Using k-Anonymity

k-anonymity is the method used for masking sensitive data which successfully solves the problem of re-linking of data with an externa l source and makes it difficul t to l'e-iden tify the individual. T hus kanonymity works on a set of quasi-identifiers (public sensitive at t ributes), whose possible availability and linking is anticipated from external dataset , and demands that the released dataset will contain at least k records for every possible quasi-identifier value. Another aspect of k is its capability of mainta ining the truthfulness of the released data (unlike other existing methods) . This is achieved by generalization, a primary technique in k-anonymity. Generalization consists of generalizing attribute values and substituting them wit h semantically consistent but less precise values. When the substituted value doesn 't preserve semantic validity the technique is called suppression which is a private case of generalization. We present a hybrid approach called compensation which is based on suppression and swapping for achieving privacy. Since swapping decreases the truthfulness of attribu te values there is a tradeoff between level of swapping (information trut hfulness) and suppression (information loss) incorporated in our algorit hm. We use k-anonymity to explore the issue of anonymity preservation. Since we do not use generalization, we do not need a priori knowledge of at t ribute semant ics. We investigate dat a anonymization in t he context of classification and use tree propert ies to satisfy k-anonymization. Our work improves previous approaches by increasing classification accuracy.