Differential privacy (DP) is a strong mathematical framework that ensures individual data points remain indistinguishable within datasets, thus protecting personal information even when subject to analysis. It is particularly relevant in quantum data sharing, where sensitive information may be processed by quantum algorithms, risking exposure if proper privacy mechanisms are not in place. Integrating DP into quantum computing can leverage unavoidable quantum noise, allowing for privacy preservation in quantum algorithms while achieving efficient performance[2][3].
However, establishing effective differential privacy in quantum environments presents challenges, such as ensuring that quantum algorithms can utilize classical DP mechanisms without compromising quantum advantages or introducing excessive computational overhead[1][5].
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