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

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dc.contributor.authorKotal, Anantaa
dc.contributor.authorElluri, Lavanya
dc.contributor.authorGupta, Deepti
dc.contributor.authorMandalapu, Varun
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2024-09-24T08:59:50Z
dc.date.available2024-09-24T08:59:50Z
dc.date.issued2023-12
dc.description2023 IEEE International Conference on Big Data (BigData), 15-18 December 2023, Sorrento, Italy
dc.description.abstractBig 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.
dc.description.urihttps://ieeexplore.ieee.org/document/10386276
dc.format.extent10 pages
dc.genreconference papers and proceedings
dc.genrepreprints
dc.identifierdoi:10.13016/m2uxwy-ndbo
dc.identifier.citationKotal, Anantaa, Lavanya Elluri, Deepti Gupta, Varun Mandalapu, and Anupam Joshi. “Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis.” In 2023 IEEE International Conference on Big Data (BigData), 5519–28, 2023. https://doi.org/10.1109/BigData59044.2023.10386276.
dc.identifier.urihttps://doi.org/10.1109/BigData59044.2023.10386276
dc.identifier.urihttp://hdl.handle.net/11603/36368
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Center for Cybersecurity
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Student Collection
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.subjectData privacy
dc.subjectCodes
dc.subjectTask analysis
dc.subjectPrivacy Attacks
dc.subjectUMBC Ebiquity Research Group
dc.subjectAgriculture
dc.subjectGeneral Data Protection Regulation
dc.subjectData Privacy
dc.subjectBig data in Agriculture
dc.subjectBig Data
dc.subjectSurveys
dc.subjectPrivacy Policy
dc.titlePrivacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-1766-3447
dcterms.creatorhttps://orcid.org/0000-0002-8641-3193

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