Improving the utility of differential privacy via Univariate Microaggregation

Abstract

Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type of queries. On the contrary, k-anonymity-like anonymization offers general purpose data releases that make no assumption on the uses of the protected data. This paper proposes a mechanism to offer general purpose differentially private data releases with a specific focus on the preservation of the utility of the protected data. Our proposal relies on univariate microaggregation to reduce the amount of noise needed to satisfy differential privacy. The theoretical benefits of the proposal are illustrated and in a practical setting.