Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. Currently, the study on combination of machine learning and DP is vibrant. In contrast, another widely used artificial intelligence technique, the swarm intelligence (SI) algorithm, has received little attention in the context of DP even though it also triggers privacy concerns. For this reason, this paper attempts to combine DP and SI for the first time, and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF). Based on the exponential mechanism, this framework can easily develop existing SI algorithms into the private versions. As examples, we apply the proposed DPSIAF to four popular SI algorithms, and corresponding analyses demonstrate its effectiveness. More interestingly, the experimental results show that, for our private algorithms, their performance is not strictly affected by the privacy budget, and one of the private algorithms even owns better performance than its non-private version in some cases. These findings are different from the conventional cognition, which indicates the uniqueness of SI with DP. Our study may provide a new perspective on DP, and promote the synergy between metaheuristic optimization community and privacy computing community.

翻译：差分隐私（DP）作为一种有前景的隐私保护模型，近年来引起了研究者的广泛兴趣。当前，机器学习与差分隐私结合的研究方兴未艾。相比之下，另一类广泛应用的人工智能技术——群体智能算法，尽管同样引发隐私问题，但在差分隐私背景下却鲜受关注。为此，本文首次尝试将差分隐私与群体智能算法相结合，并提出一种通用的差分隐私群体智能算法框架（DPSIAF）。基于指数机制，该框架可便捷地将现有群体智能算法发展为隐私保护版本。作为示例，我们将所提框架应用于四种主流群体智能算法，相应分析验证了其有效性。更有趣的是，实验结果表明：对于我们的隐私算法，其性能并不严格受隐私预算影响，甚至在某些情况下，其中一种隐私算法的性能优于其非隐私版本。这些发现异于传统认知，揭示了差分隐私与群体智能相结合的特殊性。本研究或可为差分隐私提供新视角，并促进元启发式优化领域与隐私计算领域的协同发展。