Toward a Hybrid Quantum Differential Privacy
Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos, Tianqing Zhu, Gang Li · IEEE Journal on Selected Areas in Communications · 2025
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 the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the 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 introduceLifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation.