Optimal quantum locally differentially private mechanisms in the high-privacy regime
It establishes fundamental limits and quantum advantages for privacy-preserving data analysis in the high-privacy regime, relevant to quantum information theory and differential privacy communities.
This paper optimizes the privacy-utility trade-off in the high-privacy regime for both classical and quantum local differential privacy, providing optimal mechanisms and proving that the asymptotic quantum-to-classical utility ratio is constant across different utility functions, with quantum advantages (Q/C ≥ 3/2) for n-ary data with n ≥ 3.
We optimize the trade-off between privacy and utility in the high-privacy regime. We adopt local differential privacy (LDP) and its quantum extension, quantum local differential privacy (QLDP), for privacy protection, and investigate utility functions including the Holevo information (which reduces to the mutual information in the classical case) and the error exponents in symmetric and asymmetric hypothesis testing. These utility functions have classical and quantum optimal values, which are denoted by $C$ and $Q$, respectively, in this abstract for simplicity. In this paper, we provide optimal LDP and QLDP mechanisms achieving the classical and quantum optimal values in the high-privacy regime, and prove that the asymptotic ratio $Q/C$ in this regime takes the same value regardless of the utility function. Our results reveal quantum advantages (more precisely, $Q/C\ge3/2$) for the above utility functions when the protected private data are $n$-ary with $n\ge3$.