Open research has become a central paradigm in contemporary educational research, driven by growing demands for transparency, reproducibility, and societal relevance. These practices include open access to publications, preregistration, open methodologies, and the sharing of data and analytical code throughout the research lifecycle. In education, they support cumulative knowledge building, enable replication and secondary analysis, and strengthen the empirical basis of policy and practice.

The adoption of open research principles in education is closely aligned with the FAIR data principles (Wilkinson, 2016), which emphasize that research outputs should be findable, accessible, interoperable, and reusable. In this context, research data—particularly administrative records generated by Educational Management Information Systems (EMIS)—have become a cornerstone for advancing scientific knowledge and informing public policy. EMIS data provide comprehensive population coverage, high temporal granularity, and relatively low costs for secondary research, making them especially valuable for large-scale and longitudinal educational studies.

However, the reuse of administrative educational data is often limited by tensions between the public interest in data openness and the ethical and legal obligations imposed by data protection frameworks such as the General Data Protection Regulation (GDPR) (EU, 2016). Educational research relies heavily on personal and sensitive data, including demographic information, academic records, behavioral traces, and institutional data. These risks are heightened by the involvement of minors and vulnerable populations and by the high-dimensional, linkable nature of contemporary educational datasets (Fazendeiro et al., 2025; Ferrão et al., 2022, 2023). Consequently, unregulated data sharing may lead to re-identification risks and potential harm.

Data anonymization plays a critical role in enabling open research in education while mitigating these risks. Anonymization refers to a set of technical and organizational measures designed to irreversibly prevent the identification of data subjects under reasonably foreseeable conditions (Sweeney et al., 2018; Prasser et al. 2020). Unlike pseudonymization, anonymization aims to eliminate both direct and indirect identification risks, and thus constitutes not only a technical safeguard but also a methodological and ethical requirement for responsible data sharing within Open Science frameworks.

This article reflects on the practical challenges of preparing, anonymizing, and documenting sensitive educational data for reuse in accordance with Open Science and FAIR principles. Drawing on microdata from the Brazilian National Student Performance Exam (ENADE) and the RAIDES database, provided by the Portuguese Directorate-General for Education and Science Statistics (DGEEC), the study explores the trade-offs between data anonymization and analytical utility in real-world administrative datasets. Special emphasis is placed on the impact of anonymization decisions on the interpretability and inclusiveness of educational data.

The findings indicate that widely used anonymization techniques, including advanced methods such as (ε, δ)-Differential Privacy (Dwork & Roth, 2014; Bild et al., 2018) may introduce subtle yet significant biases. Although the overall statistical structure of the data may appear to be preserved, these techniques can disproportionately suppress or distort minority groups and rare categories, particularly across racial, socio-economic, or institutional dimensions. Such distortions pose substantial risks for equity-oriented research, as the omission or misrepresentation of underrepresented populations in anonymized datasets can result in misleading analyses and compromised evidence-based policy decisions.

These results indicate that the transition toward FAIR-compliant educational data infrastructures cannot rely solely on technical solutions. Rather, it requires sustained and structured dialogue among researchers, data infrastructure providers, and domain experts to ensure that anonymization practices uphold both scientific validity and social inclusivity. We contend that the active involvement of domain experts in the interpretation of anonymized data is essential to prevent privacy-preserving technologies from inadvertently compromising the educational equity and social justice goals they are intended to advance.

The anonymization process began with a structured audit of the dataset to identify direct identifiers, quasi-identifiers, and sensitive attributes. Direct identifiers, including names, unique identification numbers, and contact information, were removed prior to analysis. Quasi-identifiers—such as age, grade level, gender, geographic indicators, and institutional characteristics—were identified based on their potential to enable re-identification when combined. Sensitive attributes, including academic achievement measures and behavioral indicators, were retained subject to appropriate transformation. A combination of anonymization techniques was applied to address different sources of disclosure risk (Mendes & Vilela, 2017; Santos et al., 2020). Generalization and suppression were used to reduce the granularity of quasi-identifiers, for example by grouping continuous variables into ranges and aggregating geographic information. These transformations were guided by k-anonymity principles (Sweeney, 2002), ensuring that each record was indistinguishable from at least k−1 other records with respect to selected quasi-identifiers. Differential Privacy (DP), a probabilistic framework that provides formal, mathematical guarantees, offers a distinct approach to privacy protection. A mechanism satisfies DP if the inclusion or exclusion of a single individual’s data does not substantially influence the output, with the level of protection governed by the privacy parameters ε (and δ). In this study, the (ε, δ)-DP model is applied to the dataset using the SafPub algorithm (Bild et al., 2018). Although most DP algorithms are designed for query-based settings in which the data custodian retains control over the data and does not release it directly (Jain et al., 2018), SafPub enables the application of DP in contexts where the goal is to publish an anonymized version of the dataset. An iterative risk–utility assessment was conducted to balance privacy protection with analytical validity. Disclosure risk was evaluated by examining equivalence class sizes, the presence of outliers, and the plausibility of linkage attacks using external data sources. Data utility was assessed by comparing key descriptive and inferential statistics between the original and anonymized datasets, including means, variances, and correlations relevant to the research questions. Anonymization parameters were adjusted iteratively until an acceptable balance between risk reduction and information loss was achieved. Finally, all anonymization decisions and transformations were systematically documented to support transparency, reproducibility, and responsible reuse. Documentation includes descriptions of removed variables, applied transformations, residual risks, and known limitations of the anonymized dataset.

This article has examined data anonymization as a foundational methodological element of open research in education. While open research practices offer substantial benefits for transparency, reproducibility, and cumulative knowledge generation, their implementation in educational contexts is constrained by the sensitive nature of educational data. Anonymization therefore serves as a critical enabling mechanism that allows data to be shared responsibly without compromising the privacy and rights of research participants. The methods described demonstrate that effective anonymization is not a one-time technical step but an iterative and context-dependent process. By combining identifier removal, generalization, suppression, and sampling-based techniques within a structured risk–utility framework, it is possible to substantially reduce re-identification risk while preserving analytical usefulness. Transparent documentation of anonymization procedures further supports reproducibility and responsible secondary use. However the results shown in (Fazendeiro et al., 2025; Ferrão et al., 2022) suggest that while the risk of reidentification is low in anonymized data, such data may be quite useless for some educational research purposes. Regardless of whether k-anonymity or differential privacy is used, anonymization of low-dimensional datasets can compromise data analysis, inferences, and research conclusions. Even in high-dimensional data, the loss or underrepresentation of minority groups may introduce bias. Therefore, it is imperative to conduct a systematic and comprehensive assessment of the limitations that the use of anonymized data imposes on educational research, and to design and implement public policies and intervention programmes aimed at mitigating these constraints, thereby strengthening quantitative educational research. Regulations governing the accreditation of researchers and access to microdata for research in the public interest should carefully balance data protection requirements with the need to advance scientific knowledge. As educational research increasingly relies on large-scale, longitudinal, and high-dimensional data, traditional de-identification methods may be insufficient, highlighting the need to explore advanced privacy-preserving techniques and secure data access infrastructures.

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