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Exploring the Application of Differential Privacy Techniques to Protect Sensitive Data in Industrial IoT Environments

EasyChair Preprint no. 13280

17 pagesDate: May 14, 2024


The rapid proliferation of Industrial Internet of Things (IIoT) devices has revolutionized industrial processes, enabling efficient data collection and analysis. However, the pervasive connectivity and large-scale data sharing in IIoT ecosystems have raised significant concerns regarding the protection of sensitive information. This abstract explores the application of differential privacy techniques as a promising solution to safeguard sensitive data in industrial IoT environments.


Differential privacy is a privacy-preserving framework that focuses on minimizing the risk of re-identifying individuals' information while allowing meaningful analysis of aggregated data. By adding controlled noise to the collected data, differential privacy techniques ensure that the statistical properties and patterns of the original data can still be extracted, while the privacy of individuals is protected.


In the context of industrial IoT, where the collection and analysis of sensitive data are essential for optimizing processes and improving productivity, the deployment of differential privacy techniques can address privacy concerns without compromising the utility of the data. These techniques can provide a robust privacy guarantee, even when adversaries have access to auxiliary information or perform sophisticated attacks.


This abstract highlights key considerations and challenges in applying differential privacy techniques to protect sensitive data in industrial IoT environments. It discusses the trade-off between privacy and data utility, as well as the impact on analytical tasks and decision-making processes. Furthermore, it explores the integration of differential privacy into existing IIoT architectures and the potential implications for system performance and resource consumption.

Keyphrases: Data Aggregation, data utility, differential privacy, Industrial IoT, privacy protection, sensitive data

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ayuns Luz and Harold Jonathan},
  title = {Exploring the Application of Differential Privacy Techniques to Protect Sensitive Data in Industrial IoT Environments},
  howpublished = {EasyChair Preprint no. 13280},

  year = {EasyChair, 2024}}
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