An Attack-Agnostic Defense Framework Against Manipulation Attacks Under Local Differential Privacy
Puning Zhao, Zhikun Zhang, Jiawei Dong, Jiafei Wu, Zhe Liu, Shaowei Wang, Yunjun Gao · 2025
Protection of local differential privacy (LDP) proto-cols against manipulation attacks is an important and challenging problem. We hope to design an attack-agnostic framework, which does not rely on any knowledge of attackers. An early work [1] restricts the attacker's capability by converting each sample into a binary signal. However, the compression of signal leads to severe loss of information, and thus results in unnecessary sacrifice of utility, especially when$\epsilon > 1$. In this paper, we propose a general estimation framework RobustLDP for robust estimation under LDP. The general idea is to send carefully crafted pre-defined information to all users, and then aggregate the feedback at the server. We strike a better tradeoff between preserving information and restricting the attacker's capability. We instantiate RobustLDP for frequency estimation and mean estimation in$\ell_{1}$and$\ell_{2}$support, which serve as building blocks for more advanced tasks. We also establish theoretical guarantees for all possible attacks. The result shows that our method significantly outperforms the existing one for$\epsilon > 1$. Extensive experiments on multiple real-world datasets validate the effectiveness of our method.