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Differentially Private Event-Triggered Sampling ?

机译：差异私有事件触发的采样 ？

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This paper describes a differentially private event-triggered sampling mechanism to select measurement samples from a data sequence whose dynamics can be modelled by a stochastic linear system. The mechanism produces subsequences that can be used to reestimate the original sequence relatively accurately and the differential privacy constraint guarantees that these subsequences are insensitive to certain variations in the input sequence. The subsampling process can be motivated by the presence of communication bandwidth constraints, but also provides an additional tool to explore achievable privacy-utility tradeoffs in privacy-preserving signal processing and control. Event-triggered sampling can offer benefits over periodic subsampling by attempting to select the most useful samples, but the fact that it leaks information when no sampling occurs must be carefully taken into account to meet the differential privacy requirement. We propose a design using a stochastic sampling threshold, leveraging the "sparse vector technique" from differential privacy to incur a privacy loss only when samples are actually released. This design includes a suboptimal but tractable recursive finite-dimensional estimator that can also be used to re-estimate the original sequence from the differentially private noisy subsequence.
机译：本文介绍了一种差异的私有事件触发的采样机制，以从数据序列中选择测量样本，其动态可以通过随机线性系统建模。该机制产生了可用于相对准确地重新定位原始序列的子句，并且差分隐私约束保证这些子序列对输入序列中的某些变化不敏感。可以通过存在通信带宽约束的存在来激励子采样过程，而是还提供了额外的工具，用于探索隐私保留信号处理和控制中可实现的隐私式权限。事件触发的采样可以通过尝试选择最有用的样本来提供周期性分支机的优势，但必须仔细考虑在没有采样时泄漏信息的事实，以满足差异隐私要求。我们提出了一种使用随机采样阈值的设计，利用差异隐私的“稀疏向量技术”，仅在实际释放样本时才会产生隐私损失。该设计包括次优但易递归有限维估计器，其也可用于从差别私有嘈杂的子序列重新估计原始序列。

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