Quantum Differentially Private Sparse Regression Learning
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, Dacheng Tao · IEEE Transactions on Information Theory · 2022
The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to solve sparse regression tasks. Concretely, given$N~d$-dimensional data points with$N\ll d$, we first prove that the optimal classical and quantum non-private Lasso requires$\Omega (N+d)$and$\Omega (\sqrt {N}+\sqrt {d})$runtime, respectively. We next prove that the runtime cost of QDP Lasso isdimension independent, i.e.,$O(N^{5/2})$, which implies that the QDP Lasso can be faster than both the optimal classical and quantum non-private Lasso. Last, we exhibit that the QDP Lasso attains a near-optimal utility bound$\tilde {O}(N^{-2/3})$with privacy guarantees and discuss the chance to realize it on near-term quantum chips with advantages.