The Dirichlet Mechanism for Differential Privacy on the Unit Simplex

TL;DR

This paper introduces a Dirichlet-based mechanism for differentially private data sharing on the unit simplex, ensuring privacy while maintaining the data within the same domain without additional projection.

Contribution

It proposes a novel Dirichlet mechanism for differential privacy on the unit simplex, with analytical privacy guarantees and a tunable privacy-variance trade-off.

Findings

Mechanism guarantees privacy for identity and average queries.

The privacy-variance trade-off can be balanced via a parameter.

Numerical results validate the theoretical analysis.

Abstract

As members of a network share more information with each other and network providers, sensitive data leakage raises privacy concerns. To address this need for a class of problems, we introduce a novel mechanism that privatizes vectors belonging to the unit simplex. Such vectors can be seen in many applications, such as privatizing a decision-making policy in a Markov decision process. We use differential privacy as the underlying mathematical framework for these developments. The introduced mechanism is a probabilistic mapping that maps a vector within the unit simplex to the same domain according to a Dirichlet distribution. We find the mechanism well-suited for inputs within the unit simplex because it always returns a privatized output that is also in the unit simplex. Therefore, no further projection back onto the unit simplex is required. We verify the privacy guarantees of the mechanism for two cases, namely, identity queries and average queries. In the former case, we derive expressions for the differential privacy level of privatizing a single vector within the unit simplex. In the latter case, we study the mechanism for privatizing the average of a collection of vectors, each of which is in the unit simplex. We establish a trade-off between the strength of privacy and the variance of the mechanism output, and we introduce a parameter to balance the trade-off between them. Numerical results illustrate these developments.