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：在现代分布式计算应用（如联邦学习和AIoT系统）中，保护隐私至关重要，以防止恶意方串通窃取他人的私有信息。然而，在保护所有方隐私的同时保证计算结果的效用性具有挑战性，尤其是当各方的隐私需求高度异质时。本文提出了一种新颖的多方计算隐私框架——阈值个性化多方差分隐私（TPMDP），该框架应对有限数量的半诚实串通敌手。该框架允许每方拥有个性化隐私预算。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LP问题的特定结构，在O(n)次计算（n为参与方数量）内求得最优解，而通用求解器可能需要指数级计算量。大量实验表明，与不考虑个性化隐私需求或串通阈值的现有隐私机制相比，本方法在低效用损失和高效率方面具有优势。