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

TL;DR

This paper introduces new metrics for assessing confidentiality and utility in privacy-preserving data publishing, based on the permutation model, and compares various anonymization techniques using these metrics.

Contribution

It proposes a unified framework of metrics for confidentiality and utility in permutation-based anonymization, enabling comparison of different privacy-preserving methods.

Findings

Metrics effectively quantify confidentiality and utility trade-offs.

Different anonymization methods show varying levels of utility and confidentiality.

The framework allows for systematic evaluation of privacy-preserving techniques.

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 conveniently traded off against the confidentiality metrics, because all of them are bounded between 0 and 1. As an application, we compare the utility-confidentiality trade-offs achieved by several anonymization approaches, including privacy models (k-anonymity and $\epsilon$-differential privacy) as well as SDC methods (additive noise, multiplicative noise and synthetic data) used without privacy models.