  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 EU 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. 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 and secure data based on applicable regulatory policy rules.
