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Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the European Union’s General Data Protection Regulation (GDPR), the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Synthetic data that retains the statistical properties of the original data but does not include actual individuals’ information provides a suitable alternative to sharing sensitive datasets. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats, such as re-identification and linkage issues, and secure data based on applicable regulatory policy rules.
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Knowledge Infusion in Privacy Preserving Data Generation
Security monitoring is crucial for maintaining a strong IT infrastructure by protecting against emerging threats, identifying vulnerabilities, and detecting potential points of failure. It involves deploying advanced tools to continuously monitor networks, systems, and configurations. However, organizations face challenges in adapting modern techniques like Machine Learning (ML) due to privacy and security risks associated with sharing internal data. Compliance with regulations like GDPR further complicates data sharing. To promote external knowledge sharing, a secure and privacy-preserving method for organizations to share data is necessary. Privacy-preserving data generation involves creating new data that maintains privacy while preserving key characteristics and properties of the original data so that it is still useful in creating downstream models of attacks. Generative models, such as Generative Adversarial Networks (GAN), have been proposed as a solution for privacy preserving synthetic data generation. However, standard GANs are limited in their capabilities to generate realistic system data. System data have inherent constraints, e.g., the list of legitimate I.P. addresses and port numbers are limited, and protocols dictate a valid sequence of network events. Standard generative models do not account for such constraints and do not utilize domain knowledge in their generation process. Additionally, they are limited by the attribute values present in the training data. This poses a major privacy risk, as sensitive discrete attribute values are repeated by GANs. To address these limitations, we propose a novel model for Knowledge Infused Privacy Preserving Data Generation. A privacy preserving Generative Adversarial Network (GAN) is trained on system data for generating synthetic datasets that can replace original data for downstream tasks while protecting sensitive data. Knowledge from domain-specific knowledge graphs is used to guide the data generation process, check for the validity of generated values, and enrich the dataset by diversifying the values of attributes. We specifically demonstrate this model by synthesizing network data captured by the network capture tool, Wireshark. We establish that the synthetic dataset holds up to the constraints of the network-specific datasets and can replace the original dataset in downstream tasks.
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- Award ID(s):
- 2114892
- PAR ID:
- 10508533
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- KDD Workshop on Knowledge-infused Learning, 29TH ACM SIGKDD
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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