In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent misbehaving parties from colluding to steal others' private information. However, guaranteeing the utility of computation outcomes while protecting all parties' privacy can be challenging, particularly when the parties' privacy requirements are highly heterogeneous. In this paper, we propose a novel privacy framework for multi-party computation called Threshold Personalized Multi-party Differential Privacy (TPMDP), which addresses a limited number of semi-honest colluding adversaries. Our framework enables each party to have a personalized privacy budget. We design a multi-party Gaussian mechanism that is easy to implement and satisfies TPMDP, wherein each party perturbs the computation outcome in a secure multi-party computation protocol using Gaussian noise. To optimize the utility of the mechanism, we cast the utility loss minimization problem into a linear programming (LP) problem. We exploit the specific structure of this LP problem to compute the optimal solution after O(n) computations, where n is the number of parties, while a generic solver may require exponentially many computations. Extensive experiments demonstrate the benefits of our approach in terms of low utility loss and high efficiency compared to existing private mechanisms that do not consider personalized privacy requirements or collusion thresholds.

翻译：在联邦学习与AIoT系统等现代分布式计算应用中，保护隐私对于防止恶意方合谋窃取他人私密信息至关重要。然而，在保护所有各方隐私的同时保证计算结果的效用性颇具挑战，尤其当各方的隐私需求高度异质时。本文提出了一种名为阈值个性化多方差分隐私（TPMDP）的新型多方计算隐私框架，该框架针对有限数量的半诚实合谋攻击者。我们的框架允许每一方拥有个性化的隐私预算。我们设计了一种易于实现且满足TPMDP的多方高斯机制，其中每一方在安全多方计算协议中使用高斯噪声扰动计算结果。为优化该机制的效用性，我们将效用损失最小化问题转化为线性规划（LP）问题。我们利用该线性规划问题的特定结构，通过O(n)次计算即可求得最优解（n为参与方数量），而通用求解器可能需要指数级计算量。大量实验证明，与未考虑个性化隐私需求或合谋阈值的现有隐私机制相比，我们的方法在低效用损失与高效率方面具有显著优势。