Enhanced Local Differential Privacy Protection Against Crosstalk Attacks in Quantum Computing

Hui Zhong, Xinyue Zhang, Hao Wang, Shucheng Yu, Yu Wang, Miao Pan

Abstract: Quantum computing has gained widespread interest due to its exponential computational capabilities. In practical scenarios, users often access real quantum computers indirectly through cloud-based platforms (e.g., IBM Quantum), which requires transmitting data to third-party servers. Quantum-specific attacks, such as crosstalk attacks, have demonstrated high success rates in inferring the output of legitimate users. These issues raise serious privacy concerns. To protect client-side privacy, quantum local differential privacy (QLDP) has been proposed, where legitimate users perturbed their true output by adding quantum noise to the circuits. However, we observe that the classical local differential privacy (LDP) properties have not been fully adapted to the quantum domain, and the information can still be inferred from the perturbed output if attackers access the noise type added by legitimate users. To fill this gap, we propose a novel QLDP-based approach to protect the true output of legitimate users. We find that QLDP can be achieved using only simple quantum noise, but not all types of quantum noise can effectively perturb the output under different quantum measurements. In addition, to prevent advanced attackers who have partial user information, we introduce a probabilistic noise addition mechanism. To allow legitimate users to accurately estimate the true output of a quantum circuit, we also propose a new quantum frequency estimation. Our approach is validated using real quantum computers and quantum simulators, achieving 94% accuracy and 90% utility to estimate the true output from the perturbed output.

External IDs:dblp:conf/qce/ZhongZWYWP25