Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving
This work provides a new perspective on differential privacy by showing potential optimization benefits in swarm intelligence, which could bridge the metaheuristic optimization and privacy computing communities.
The paper introduces a differentially private swarm intelligence algorithm framework (DPSIAF) to address privacy concerns in swarm intelligence, and finds that in some cases, the private algorithms perform as well as or better than non-private versions, with one algorithm showing improved performance under certain conditions.
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.