Privacy-Preserving Federated Analytics for Collaborative Institutional Research

Matteo R. Bellini

Privacy-Preserving Federated Analytics for Collaborative Institutional Research

Author(s)	Dr. Matteo R. Bellini
Country	United States
Abstract	Collaborative institutional research can generate stronger evidence about student progression, retention, curriculum effectiveness, access, academic support, and resource utilization than isolated analysis within individual institutions. Yet cross-institutional research frequently requires access to sensitive student-level records, creating substantial privacy, governance, security, and organizational barriers to conventional centralized data pooling. This paper examines privacy-preserving federated analytics as an alternative architecture for collaborative institutional research. Federated analytics enables participating institutions to perform approved computations locally and contribute restricted intermediate results to an aggregation process while retaining raw records within their own information environments. The study adopts a conceptual-methodological research design grounded in contemporary developments in federated computation, secure aggregation, differential privacy, cross-silo learning, and privacy attacks. The proposed architecture combines local query execution, schema harmonization, contribution bounding, secure aggregation, minimum-participation thresholds, differential privacy, query authorization, privacy-budget management, audit logging, and disclosure review. Foundational secure-aggregation research demonstrates that distributed parties can compute aggregate values without revealing each participant's private input, while subsequent research shows that aggregated updates may still become vulnerable to reconstruction or disaggregation under inappropriate threat models. Differential privacy provides an additional formal mechanism for limiting the influence of individual records on released statistics, although stronger privacy generally introduces a measurable utility cost. The analysis shows that federated institutional research is especially well suited to counts, proportions, means, histograms, frequency estimation, benchmark indicators, and selected statistical models, provided that query outputs are sufficiently aggregated and protected. The proposed framework also emphasizes that technical privacy mechanisms cannot substitute for common data definitions, institutional governance, purpose limitation, transparent participation agreements, bias assessment, and appropriate interpretation of heterogeneous institutional populations. The paper concludes that federated analytics can support a transition from “share the data to conduct research” toward “share approved computations and protected aggregate evidence.” Its strongest value lies not in making sensitive data risk-free, but in reducing unnecessary centralization while preserving opportunities for collaborative, reproducible, and privacy-conscious institutional research
Keywords	Federated analytics; institutional research; privacy-preserving analytics; secure aggregation; differential privacy; higher education analytics; collaborative research; cross-silo analytics; data governance; student data privacy
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2020
Published On	2020-02-25

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American Journal of Advanced Multidisciplinary Innovation and Research

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Privacy-Preserving Federated Analytics for Collaborative Institutional Research

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