论文标题
与签名网络的差异私人双方共识，并带有随时间变化的声音
Differentially Private Bipartite Consensus over Signed Networks with Time-Varying Noises
论文作者
论文摘要
本文调查了签名网络上差异私人的两分之一的共识算法。提出的算法通过在合作竞争性的交互式信息中添加噪声来保护每个代理的敏感信息。为了获得隐私保护，允许增加噪声的差异，并且与现有作品大不相同。另外，添加噪声的方差可能是衰减或恒定的。通过使用基于随机近似方法的时变台阶尺寸，我们表明该算法在均方根和几乎呈现的隐私噪声中也会收敛。我们进一步开发了一种设计步骤大小和噪声参数的方法，从而提供算法以获得渐近无偏的两部分共识，并具有所需的准确性和预定义的差异隐私水平。此外，我们给出了算法的均值和几乎呈现的融合率，以及具有不同形式的隐私声音的隐私级别。我们还揭示了算法在收敛率和隐私水平之间的权衡。最后，一个数值示例验证了理论结果，并证明了算法对现有方法的优势。
This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed algorithm protects each agent's sensitive information by adding noise with time-varying variances to the cooperative-competitive interactive information. In order to achieve privacy protection, the variance of the added noise is allowed to be increased, and substantially different from the existing works. In addition, the variance of the added noise can be either decaying or constant. By using time-varying step-sizes based on the stochastic approximation method, we show that the algorithm converges in mean-square and almost-surely even with an increasing privacy noise. We further develop a method to design the step-size and the noise parameter, affording the algorithm to achieve asymptotically unbiased bipartite consensus with the desired accuracy and the predefined differential privacy level. Moreover, we give the mean-square and almost-sure convergence rate of the algorithm, and the privacy level with different forms of the privacy noises. We also reveal the algorithm's trade-off between the convergence rate and the privacy level. Finally, a numerical example verifies the theoretical results and demonstrates the algorithm's superiority against existing methods.
