General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model

Identifier:  imarina:9052334

Handle: https://hdl.handle.net/20.500.11797/imarina9052334

Authors:  Domingo-Ferrer, Josep; Muralidhar, Krishnamurty; Bras-Amoros, Maria

Abstract:
Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to this model, any SDC method for individual data records is functionally equivalent to a permutation step plus a noise addition step, where the noise added is marginal, in the sense that it does not alter ranks. Here, we propose metrics to quantify the data confidentiality and utility achieved by SDC methods based on the permutation model. We distinguish two privacy notions: in our work, anonymity refers to subjects and hence mainly to protection against record re-identification, whereas confidentiality refers to the protection afforded to attribute values against attribute disclosure. Thus, our confidentiality metrics are useful even if using a privacy model ensuring an anonymity level ex ante. The utility metric is a general-purpose metric that can be …