Tackling Privacy-Related Concerns for Student Data in Education Research
SafeInsights Team
Published:November 9, 2026•11 min read
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Education research has enormous potential to transform how students learn, but it comes with a fundamental challenge: student data is deeply personal, highly regulated, and rightfully protected. More specifically, privacy-related concerns when using student data exist because:
Researchers accessing individual student records increase exposure risk
Data must be extracted and transferred between organizations, creating multiple vulnerability points
Each data request requires custom legal reviews and privacy assessments, slowing research to a crawl
Students and families have limited visibility into how their educational data is being used
Family Educational Rights and Privacy Act (FERPA) compliance becomes complex when data changes hands between institutions
Data breaches or misuse can have serious consequences for institutions and for the leaders who approved the sharing
Privacy risks lead to researchers having limited access to the data they need
Traditional research methods require moving data outside the systems where it lives, creating privacy concerns that have limited the scope, scale, and impact of education research for decades.
SafeInsights was built specifically to solve this problem by bringing research questions to the data instead of bringing data to researchers, making large-scale, privacy-preserving research not just possible, but practical.
Contact SafeInsights and bring your research questions to data that has been out of reach.
Direct researcher access to identifiable student records
In traditional research models, researchers can receive direct access to student records that include names, grades, demographic information, and detailed learning behaviors. This creates an inherent vulnerability: even with the best intentions and strict protocols, human error happens. A researcher's laptop gets stolen, a file gets accidentally shared, or data sits unencrypted on a personal device. The more people who can view identifiable information, the more exposure points exist.
While most researchers handle data responsibly, the fundamental structure of giving external parties access to sensitive records creates student privacy concerns that even the most careful protocols cannot fully eliminate.
How SafeInsights helps: SafeInsights removes the largest source of this risk: researchers never see raw student data — they submit analysis code that runs in secure enclaves inside the data organization's own systems, receiving only aggregate results that cannot identify individual students.
Data extraction and transfer between organizations
Beyond the researcher's individual hardware, every time student data moves from one system to another, it creates a new opportunity for exposure. Traditional research requires extracting data from its original source — whether that's a learning management system, an adaptive platform, or a state longitudinal database — and transferring it to the researcher's institution.
This process involves multiple steps: exporting files, encrypting them, transmitting them through email or file transfer systems, storing them on research servers, and eventually archiving or destroying them.
Each transition point represents a potential vulnerability, and each organization in the chain must maintain robust student data protection measures.
How SafeInsights helps: With SafeInsights, data never leaves the systems where it already securely lives. Research questions come to the data through code execution in secure data enclaves, so the export, transfer, storage, and destruction steps that create risk in conventional approaches do not happen. What remains is the organization's own environment, which it already secures.
Time-consuming legal and privacy reviews for each data request
When state or local education agencies receive a research request, it can trigger a months-long review process involving legal counsel, privacy officers, IT security teams, and often external consultants. Each stakeholder must assess risks, review contracts, negotiate terms, and sign off on data-sharing agreements before any research can begin.
For a PhD student, a process that commonly runs from several months to more than a year can derail an entire dissertation. For state education departments managing a steady stream of research requests, the administrative burden becomes unsustainable.
These delays don't reflect bureaucratic inefficiency; they reflect the genuine complexity of ensuring compliance when data changes hands.
How SafeInsights helps: SafeInsights replaces per-study negotiation with a standard framework that an organization's legal team reviews once. Each new study is then reviewed against criteria the organization has already agreed to, rather than through a new contract. The goal is to move approval from months to weeks without weakening oversight.
Limited transparency for students and families
Most students and families have little visibility into whether and how their educational data is used for research. Traditional data-sharing agreements happen between institutions and researchers, with minimal visibility for the individuals whose information is being studied. Even when consent forms exist, they're often buried in terms of service documents or presented as optional checkboxes that students click through without reading.
This lack of transparency erodes trust and raises legitimate student privacy issues about who controls educational data and how it's used. It also affects research quality. When learners perceive privacy risk, they are more likely to opt out or withhold information, which changes the data researchers see (Ito et al., 2026; Mutimukwe et al., 2022).
How SafeInsights helps: Because data never leaves institutional systems when using SafeInsights, schools and districts can provide families with straightforward explanations: research happens through approved analysis code running on protected data, not through sharing personal records with outside parties.
Increased complexities in FERPA compliance
FERPA adds a layer of complexity every time identifiable data change hands. Disclosing personally identifiable information (PII) from education records without consent requires an exception, (most often the studies exception (34 CFR 99.31(a)(6)); U.S. Department of Education, n.d.) which in turn requires a written agreement specifying the study's purpose, limits on redisclosure, and a date by which the recipient must destroy the data. Each receiving organization takes on those obligations.
When an educational institution shares data with a researcher at another institution, both entities must ensure FERPA security requirements are met, creating shared liability and administrative overhead. Multi-state studies compound this complexity, as researchers must navigate different state interpretations of federal law alongside varying institutional policies. And as more datasets are added to the mix, the risk of reidentifying individual student information increases.
How SafeInsights helps: SafeInsights simplifies compliance by keeping data with its original custodian. Because researchers receive de-identified, aggregate results, the redisclosure and destruction obligations that follow identifiable data do not attach to the research team. The custodian's existing FERPA responsibilities stay where they are.
Severe consequences resulting from data breaches or misuse
The consequences of student data exposure extend far beyond regulatory fines. A U.S. Government Accountability Office review (2020) of 99 reported K-12 data breaches between 2016 and 2020 found that compromised records included grades, special education information, and Social Security numbers, leaving students exposed to financial, emotional, and in some cases physical harm. The institutional fallout follows: lost trust, investigations, and leaders who become reluctant to approve any further data sharing.
When a researcher's laptop containing student records is stolen or a university server is hacked, the fallout affects everyone involved: students whose privacy was violated, parents who lose trust in institutions, administrators who face disciplinary action, and the research community whose access gets restricted further.
These aren't hypothetical scenarios; they're documented incidents that have made institutional leaders understandably risk-averse about any data sharing. Research files represent one share of the incidents (most of the breaches came from district systems, staff error, and outside attackers) but every incident raises the bar for the next data request. The traditional model puts all parties in a vulnerable position where a single security failure anywhere in the research chain can trigger devastating consequences.
How SafeInsights helps: SafeInsights reduces this category of risk. Identifiable data are never copied out to researchers, so there is no research dataset on a laptop or a university server to lose. The data organization's own security, which it already maintains, remains the primary perimeter.
Restricted access that weakens research validity due to privacy concerns
Ironically, privacy protections designed to safeguard students often undermine the quality of research meant to help them. When researchers can only access small, convenience samples from individual institutions willing to share data, studies suffer from selection bias, limited statistical power, and questionable generalizability.
A study of 300 students in three schools can answer some questions well, but it cannot show whether a finding holds for different students, schools, and tools. Constraints like these are standard in education research. Reproducibility suffers when each study uses a one-off extract that no one else can access to verify the findings.
This restricted access doesn't just slow progress — it fundamentally limits what questions researchers can answer and how confident we can be in the results.
How SafeInsights helps: SafeInsights lets researchers study large groups of students over time, across educational apps and institutions, within strong privacy protections. Because analyses run as code inside an enclave, another researcher can run the same code for a form of reproducibility the traditional model cannot offer.
Student privacy concerns in research — FAQs
What is student data privacy?
Student data privacy is the protection of personally identifiable information (PII) collected during education, including academic records, demographics, and learning analytics. Under federal and state regulations, institutions must safeguard this information from unauthorized access and use it only for legitimate educational purposes with appropriate consent and security measures.
How is student data protected in education research?
Traditional methods use data-sharing agreements, deidentification, anonymization, pseudonymization, and encryption, along with Institutional Review Board (IRB) oversight, but still require moving data outside secure environments. SafeInsights turns this process around by bringing research questions to the data where it already lives, removing the risks associated with data extraction while maintaining FERPA compliance.
What are FERPA requirements for research using student data?
FERPA requires written consent before disclosing personally identifiable student information, with limited exceptions for research purposes (including the studies exception at 34 CFR 99.31(a)(6)). Institutions must ensure researchers sign agreements, use data only for stated purposes, and destroy data when it is no longer needed for the study. SafeInsights simplifies compliance because researchers never receive PII; they receive only de-identified and aggregate results.
How does SafeInsights ensure student data never leaves institutional systems?
Instead of the traditional approach of taking data out of educational apps and websites for researchers to study, SafeInsights brings the researchers' questions to the data. We provide the infrastructure for institutions and educational apps to use secure data enclaves to process research questions directly in their own systems.
The researchers don't have access to the data itself. Instead, they package up their questions as computer code and send them to these enclaves for the institutions to review and approve. Only aggregate, de-identified results are returned, so researchers have no path to individual student records.
Can student data be re-identified from research results?
This is why SafeInsights interweaves technical protections alongside governance mechanisms to protect not only inputs, but also results. SafeInsights implements multiple protection layers: researchers use sample data to code their analyses, while data providers review all code and results before release, suppress small sample outputs, and apply statistical disclosure control techniques, so that research outputs do not reveal individual student data.
Start your research with a privacy-preserving infrastructure built for the future
The privacy concerns outlined in this article, from direct researcher access to identifiable records and data transfer vulnerabilities to severe breach consequences, are fundamental challenges that have constrained education research for decades.
SafeInsights offers a fundamentally different approach by bringing research questions to data rather than moving data to researchers. This U.S. National Science Foundation (NSF)-funded infrastructure reduces privacy risks by eliminating data extraction, while enabling researchers to run studies on educational data representing millions of students.
Educational institutions, state education agencies, and educational apps keep control of their data with reduced operational burden. Most importantly, students benefit from evidence-informed improvements to teaching and learning without their personal information ever leaving secure systems.
Start your research through SafeInsights and bring your questions to data that has been out of reach.
References
Ito, H., Hsu, C.-Y., & Ogata, H. (2025). Too detailed to share? Towards risk-based privacy protection of fine-grained educational data. In Proceedings of the International Conference on Computers in Education (ICCE 2025). Asia-Pacific Society for Computers in Education. https://doi.org/10.58459/icce.2025.5958
Ito, H., Hsu, C.-Y., & Ogata, H. (2026). The third-party access effect: An overlooked challenge in secondary use of educational real-world data (arXiv:2601.22472). arXiv. https://doi.org/10.48550/arXiv.2601.22472
Mutimukwe, C., Viberg, O., Oberg, L.-M., & Cerratto-Pargman, T. (2022). Students’ privacy concerns in learning analytics: Model development. British Journal of Educational Technology, 53(4), 932–951. https://doi.org/10.1111/bjet.13234
U.S. Department of Education, Student Privacy Policy Office. (n.d.). 34 CFR Part 99—Family educational rights and privacy. https://studentprivacy.ed.gov/ferpa
U.S. Government Accountability Office. (2020). Data security: Recent K-12 data breaches show that students are vulnerable to harm (GAO-20-644). https://www.gao.gov/products/gao-20-644
Yacobson, E., Fuhrman, O., Hershkovitz, S., & Alexandron, G. (2021). De-identification is insufficient to protect student privacy, or – what can a field trip reveal? Journal of Learning Analytics, 8(2), 83–92. https://doi.org/10.18608/jla.2021.7353
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