Towards A Hybrid Quantum Differential Privacy

• URL: http://arxiv.org/abs/2501.07844v2
• Date: Wed, 15 Jan 2025 15:10:13 GMT
• Title: Towards A Hybrid Quantum Differential Privacy
• Authors: Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos, Tianqing Zhu, Gang Li,
• Abstract summary: 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.
• Score: 22.644150711891008
• License:
• 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.

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