Detecting privacy and ethical sensitivity in data mining results

Abstract

Knowledge discovery allows considerable insight into data. This brings with it the inherent risk that what is inferred may be private or ethically sensitive. The process of generating rules through a mining operation becomes an ethical issue when the results are used in decision making processes that affect people, or when mining customer data unwittingly compromises the privacy of those customers. Significantly, the sensitivity of a rule may not be apparent to the miner, particularly since the volume and diversity of rules can often be large. However, given the subjective nature of such sensitivity, rather than prohibit the production of ethically and privacy sensitive rules, we present here an alerting process that detects and highlights the sensitivity of the discovered rules. The process caters for differing sensitivities at the attribute value level and allows a variety of sensitivity combination functions to be employed. These functions have been tested empirically and the results of these tests are reported.