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

2026-06-11 · John Fields, K M Sajjadul Islam, Ruchitha Thota, Victor Chen, Praveen Madiraju arxiv

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 https://github.com/jtfields/NAIRR240195-Privacy-Preserving-Machine-Learning.

📄 PDF Abstract BibTeX arXiv:2606.12845

Code (0)

등록된 구현이 없습니다.

Tasks

Synthetic Data Generation Federated Learning

Privacy-Preserving Anomaly Detection in Stochastic Dynamical Systems: Synthesis of Optimal Gaussian Mechanisms

2022-11-07 · Haleh Hayati, Carlos Murguia, Nathan van de Wouw

We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy. We consider the problem setting in which a remote station seeks to identify anomalies u…

Anomaly Detection Privacy Preserving

RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service

2024-12-17 · Yihang Cheng, Lan Zhang, Junyang Wang, Mu Yuan 외

Retrieval-augmented generation (RAG) improves the service quality of large language models by retrieving relevant documents from credible literature and integrating them into the context of the user query. Recently, the …

Privacy Preserving RAG Retrieval Retrieval-augmented Generation

Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models

2025-07-10 · Xinyu Huang, Leming Shen, Zijing Ma, Yuanqing Zheng

Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home a…

Privacy Preserving Small Language Model

Token-Level Privacy in Large Language Models

2025-03-05 · Re'em Harel, Niv Gilboa, Yuval Pinter

The use of language models as remote services requires transmitting private information to external providers, raising significant privacy concerns. This process not only risks exposing sensitive data to untrusted servic…

Privacy Preserving Semantic Similarity Semantic Textual Similarity

Privacy-Preserving Causal Inference via Inverse Probability Weighting

2019-05-29 · Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet

The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often invo…

Causal Inference Econometrics Privacy Preserving