Differentially Private Interval Observer Design with Bounded Input Perturbation

Degue, Kwassi H.; Ny, Jérôme Le

doi:10.23919/acc45564.2020.9147726

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…{kdegue, karthikg, maxli, hamsa}@mit.edu data are known to be inefficient in preserving privacy [6], [7]. The notion of differential privacy [8]- [10] provides a much stronger privacy guarantee to individuals' data, and lends itself to several applications [11]- [16]. It provides each individual agent with the guarantee that the output of the considered query will not be significantly altered by whether or not they contribute their data, or what value they contribute.…”

Section: Introductionmentioning

confidence: 99%

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Differentially Private Outlier Detection in Correlated Data

Degue

Gopalakrishnan

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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The detection of outliers has become increasingly important for the control and monitoring of large-scale networked systems such as transportation and smart grids. Data from these systems, such as location traces or power consumption, are collected from individual agents, and are often privacy-sensitive. Furthermore, the networked nature of these systems results in the data of different individuals being correlated with each other. In this paper, we use the concept of differential privacy to design a privacy-preserving algorithm for outlier detection in correlated data. We determine analytic formulas to evaluate the performance of the proposed differentially private algorithm, and we analyze the trade-off between privacy level and detection accuracy. We illustrate our methodology using an example based on outlier detection in household electricity usage data.

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Section: Introductionmentioning

confidence: 99%

“…scalar Gaussian random variables differed from a given value. However, data from different agents were assumed to be uncorrelated in these prior works, which may be an unrealistic assumption in some systems [12], [16], [25].…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Outlier Detection in Correlated Data

Degue

Gopalakrishnan

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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“…random variables. However, these prior works assume that the privacy-sensitive data provided by each individual are i.i.d., which may not be the case in data generated from networked systems where individuals' data are correlated [5], [8], [17].…”

mentioning

confidence: 97%

Differentially Private Outlier Detection in Multivariate Gaussian Signals

Degue

Gopalakrishnan

et al. 2021

2021 American Control Conference (ACC)

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The detection of outliers in data, while preserving the privacy of individual agents who contributed to the data set, is an increasingly important task when monitoring and controlling large-scale systems. In this paper, we use an algorithm based on the sparse vector technique to perform differentially private outlier detection in multivariate Gaussian signals. Specifically, we derive analytical expressions to quantify the trade-off between detection accuracy and privacy. We validate our analytical results through numerical simulations.

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Cooperative Differentially Private LQG Control With Measurement Aggregation

Degue¹,

2023

IEEE Control Syst. Lett.

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Differentially Private Interval Observer Design with Bounded Input Perturbation

Cited by 4 publications

References 15 publications

Differentially Private Outlier Detection in Correlated Data

Differentially Private Outlier Detection in Correlated Data

Differentially Private Outlier Detection in Multivariate Gaussian Signals

Cooperative Differentially Private LQG Control With Measurement Aggregation

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