# Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis

arXiv: https://arxiv.org/abs/2311.15460 · HTML: https://mlsanity.com/paper/2311.15460/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare](https://mlsanity.com/paper/2111.12241/) (2021)
- [Change Management using Generative Modeling on Digital Twins](https://mlsanity.com/paper/2309.12421/) (2023)
- [Future Smart Connected Communities to Fight COVID-19 Outbreak](https://mlsanity.com/paper/2007.10477/) (2020)
- [From t-closeness to differential privacy and vice versa in data anonymization](https://mlsanity.com/paper/1512.05110/) (2015)
- [Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT](https://mlsanity.com/paper/2103.15245/) (2021)
- [Detecting Anomalous User Behavior in Remote Patient Monitoring](https://mlsanity.com/paper/2106.11844/) (2021)

## Built on by (most cited first, 5 of 5)

- [KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation](https://mlsanity.com/paper/2405.16476/) (2024)
- [Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia](https://mlsanity.com/paper/2504.00652/) (2025)
- [Enhancing Smart Farming Through Federated Learning: A Secure, Scalable, and Efficient Approach for AI-Driven Agriculture](https://mlsanity.com/paper/2509.12363/) (2025)
- [When Privacy Isn't Synthetic: Hidden Data Leakage in Generative AI Models](https://mlsanity.com/paper/2512.06062/) (2025)
- [Differentially Private Synthetic Data Generation Using Context-Aware GANs](https://mlsanity.com/paper/2512.08869/) (2025)

## Abstract (arXiv)

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.