Controlling inference: avoiding p -level reduction during analysis
Adepele Williams, Ken E. Barker · 2007
This paper presents a concept hierarchy-based approach to privacy preserving data collection for data mining called the P-level model. The P-level model allows data providers to divulge information at any chosen privacy level (P-level), for any attribute. Data collected at a high P-level signifies divulgence at a higher conceptual level and thus ensures more privacy. Providing anonymity guarantees, such as k-anonymity, prior to release can further protect the collected data set from privacy breaches due to linking the released data set with external data sets. However, the data mining process, which involves the integration of various data values, can constitute a privacy breach if combinations of attributes at certain P-levels result in the inference of knowledge that exists at a lower P-level. This paper describes the P-level reduction phenomenon and proposes methods to identify and control the occurrence of this privacy breach.