We consider a multiagent linear time-invariant system whose dynamical model may change from one disturbance event to another. The system is monitored by a control center that collects output measurements from the agents after every event and estimates the eigenvalues of the model to keep track of any adverse impact of the disturbance on its spectral characteristics. Sharing measurements in this way, however, can be susceptible to privacy breaches. If an intruder gains access to these measurements, she may estimate the values of sensitive model parameters and launch more severe attacks. To prevent this, we employ a differential privacy framework by which agents can add synthetic noise to their measurements before sending them to the control center. The noise is designed carefully by characterizing the sensitivity of the system so that it limits the intruder from inferring any incremental change in the sensitive parameters, thereby protecting their privacy. Our numerical results show that the proposed design results in marginal degradation in eigenvalue estimation when compared to the error incurred by the intruder in identifying the sensitive parameters.

中文翻译：

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网络识别的差异隐私

我们考虑一个多主体线性时不变系统，其动力学模型可能从一个扰动事件变为另一个扰动事件。该系统由控制中心监控，该控制中心在每次事件后收集代理商的输出测量值并估算模型的特征值，以跟踪干扰对其频谱特征的任何不利影响。但是，以这种方式共享测量结果可能会违反隐私规定。如果入侵者可以使用这些度量，则她可以估算敏感模型参数的值并发起更严重的攻击。为了防止这种情况，我们采用了差分隐私框架，通过该框架，代理可以在将测量结果发送到控制中心之前对其进行添加合成噪声。通过表征系统的灵敏度来精心设计噪声，以限制入侵者推断敏感参数的任何增量变化，从而保护其隐私。我们的数值结果表明，与入侵者在识别敏感参数时所产生的误差相比，所提出的设计导致特征值估计的边际退化。