The agents in a Multi-Agent System (MAS) make observations about the system and send that information to a fusion center. The fusion center aggregates the information and concludes about the system parameters with as much accuracy as possible. However for the purposes of better efficiency of the system at large, the agents need to append some private parameters to the observed data. In this scenario, the data sent to the fusion center is faced with privacy risks. The data communicated to the fusion center must be secured against data privacy breaches and inference attacks in a decentralized manner. However, this in turn leads to a loss of utility of the data being sent to the fusion center. We quantify the utility and privacy of the system using Cosine similarity. We formulate our MAS problem in terms of deducing a concept for which compression-based methods are there in literature. Next, we propose a novel sanitization mechanism for our MAS using one such compression-based method while addressing the utility-privacy tradeoff problem.

翻译：在多智能体系统（MAS）中，智能体对系统进行观测并将信息发送至融合中心。融合中心聚合这些信息，以尽可能高的精度推断系统参数。然而，为提升整体系统效率，智能体需要在观测数据中附加若干私有参数。在此场景下，传输至融合中心的数据面临隐私风险。必须以去中心化的方式保障通信数据的安全性，防止隐私泄露与推理攻击。但这又会导致传输至融合中心的数据效用降低。本文采用余弦相似度量化系统的效用与隐私水平。我们将MAS问题形式化为概念推断问题，该领域已有基于压缩方法的研究基础。随后，我们提出一种新颖的MAS净化机制，该方法运用压缩技术解决效用与隐私的权衡问题。