In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others' private information. However, guaranteeing the utility of computation outcomes while protecting all parties' data 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，以防止恶意方合谋窃取他人的隐私信息。然而，在保护所有参与方数据隐私的同时，保证计算结果的效用性仍面临挑战，尤其是当参与方的隐私需求具有高度异质性时。本文提出了一种新的多方计算隐私框架，称为阈值个性化多方差分隐私（TPMDP），该框架可应对有限数量的半诚实合谋攻击者。该框架允许每个参与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，其中每个参与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，并利用该LP问题的特殊结构，在O(n)次计算内求得最优解（n为参与方数量），而通用求解器可能需要指数级计算量。大量实验表明，与不考虑个性化隐私需求或合谋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显著优势。