Privacy-enhancing Methods for Secure Sharing of Land and Environmental Data across Collaborative Research Institutions and Open Data Ecosystems
Busola Motunrayo Olawale
*
Agrogeology and Technology Researcher, Ladoke Akintola University of Technology. Along Oyo, Ilorin Road, 210214, Ogbomoso, Oyo state, Nigeria.
Utin Nyimeobong Archibong
Information Governance Researcher, Liberty University, 1971 University Blvd, VA 24515, Lynchburg, United States of America.
Nanyeneke Ravana Mayeke
Information Technology Researcher, University of the Cumberlands, 104 Maple Drive, KY 40769, Williamsburg, United States of America.
Abiola Omolola Bamsa
Business Analytics Researcher, University of the Cumberlands, 104 Maple Drive, KY 40769, Williamsburg, United States of America.
Omobolaji Olufunmilayo Olateju
Agricultural Technology Researcher, University of Ibadan, Oduduwa Road, Ibadan, Oyo State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
This research developed and evaluated hybrid privacy-enhancing technologies (PETs) for secure sharing of land use and land cover (LULC) data across collaborative research institutions and open data ecosystems. The work was motivated by privacy risks, including re-identification via linkage attacks, inference of sensitive attributes, and data-sovereignty concerns in geospatial environmental datasets. A comprehensive literature review established the theoretical foundations of federated learning (FL), differential privacy (DP), secure multi-party computation (SMPC), homomorphic encryption (HE), and blockchain. The methodology employed a quantitative, simulation-based design. Because access to real institutional archives was beyond the scope of this study, synthetic Sentinel-2-like patches were generated programmatically and augmented with EuroSAT and NLCD-derived class schemas. These were partitioned into non-IID institutional silos to emulate heterogeneous collaborations. Hybrid PET pipelines integrated FL with DP via Opacus, SMPC aggregation, HE verification, and blockchain-based audit logging of model updates. Evaluation used accuracy, F1-score, the (ε, δ) budget tracked by a Rényi accountant, communication cost, and statistical tests. Following the standard DP convention, a smaller ε corresponds to more added noise and stronger privacy, while a larger ε relaxes privacy in exchange for utility. Results revealed substantial privacy-utility trade-offs and confirmed the expected inverse relationship between privacy level and utility. The unusually high centralized accuracy and the near-zero vanilla-FL accuracy are interpreted as artifacts of the small synthetic corpus and the lightweight model, rather than realistic estimates. The study addresses key gaps by providing practical blueprints for secure LULC collaboration. Conclusions affirm the potential of PETs, while highlighting the architectural refinements required for deployment at scale. Recommendations include scaling to real archives, mitigating non-IID heterogeneity, adopting post-quantum-ready cryptography, and embedding indigenous data-sovereignty safeguards into PET design.
Keywords: Privacy-enhancing technologies, federated learning, differential privacy, land use land Cover, open data ecosystems