A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction

Computer Science > Cryptography and Security [Submitted on 11 Jun 2026] A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction John Fields, K M Sajjadul Islam, Ruchitha Thota, Victor Chen, Praveen Madiraju This study explores privacy-preserving machine learning (PPML) techniques using the PySyft platform to enable collaborative prediction of student retention between institutions. We developed a remote data science (RDS) framework with a semi-air-gapped architecture consisting of high-side and low-side servers, allowing researchers from three universities to build predictive models on sensitive student data without direct data access. Using historical data from a small private university (N=720), we evaluated three synthetic data generation approaches and validated the framework through inter-institutional collaboration. The results demonstrate consistent classification performance across institutions (Macro F1: 0.690--0.695) while maintaining strict Family Educational Rights and Privacy Act (FERPA) compliance. We also propose Data-Type-Aware Templates, a novel synthetic data method that prioritizes privacy over distributional fidelity. Our findings confirm that RDS-based PPML is technically feasible for educational settings and offers a practical alternative to federated learning for small-scale inter-institutional collaborations. The code is available at this https URL. Comments: 7 pages, 2 figures. Accepted at the 2026 IEEE International Conference on Information Reuse and Integration (IEEE IRI 2026) Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2606.12845 [cs.CR]   (or arXiv:2606.12845v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.12845 Focus to learn more Submission history From: John Fields [view email] [v1] Thu, 11 Jun 2026 03:18:50 UTC (390 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)