Towards A Hybrid Quantum Differential Privacy

TL;DR

This paper advances quantum differential privacy by developing hybrid mechanisms that utilize multiple noise sources, enhancing privacy and utility in quantum computing systems.

Contribution

It introduces a resilient hybrid QDP mechanism combining channel and measurement noise, and proposes Lifted QDP for better privacy audits and algorithm evaluation.

Findings

Hybrid QDP mechanism effectively balances privacy and utility.

Utilizing multiple noise sources improves privacy robustness.

Lifted QDP enhances randomness for privacy audits.

Abstract

Quantum computing offers unparalleled processing power but raises significant data privacy challenges. Quantum Differential Privacy (QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights teh need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect teh multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduce Lifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation.