Application of data anonymization in Learning Analytics

Chicaiza, Janneth; Cabrera-Loayza, Ma. Carmen; Elizalde, Rene; Piedra, Nelson

doi:10.1145/3378184.3378229

Cited by 6 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, techniques to anonymize geolocation data will differ from those for anonymizing usernames, and two platforms may generate different formats for the same data attribute. Third, automatically identifying sensitive attributes in a dataset is a complex problem, meaning that researchers need to be familiar with a dataset and its schema beforehand [13]. Several techniques have been proposed to address these challenges.…”

Section: B Anonymizationmentioning

confidence: 99%

“…Finally, t-closeness [17] adds the property that the distribution of sensitive values has to be similar for each of these sets. Although previously proposed LA data management solutions include different anonymization strategies in their design [13], [18], there is no approach that generalizes to the diversity of formats and data types present on education platforms. We address this gap by proposing a desktop tool compatible with different data types and algorithms for anonymization.…”

Section: B Anonymizationmentioning

confidence: 99%

See 1 more Smart Citation

An End-to-End Data Pipeline for Managing Learning Analytics

Farah

Machado

Cunha

et al. 2021

2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET)

View full text Add to dashboard Cite

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tools that support researchers with these tasks and present a blueprint for how these tools can be integrated with existing platforms, enabling researchers to run studies within learning environments, adhere to legal and ethical privacy frameworks, and share their anonymous or anonymized data with a wider audience. We demonstrate the integration of these features into an existing online learning platform.

show abstract

Section: B Anonymizationmentioning

confidence: 99%

Section: B Anonymizationmentioning

confidence: 99%

An End-to-End Data Pipeline for Managing Learning Analytics

Farah

Machado

Cunha

et al. 2021

2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET)

View full text Add to dashboard Cite

show abstract

“…Também para dados médicos, em Lee et al (2017) é apresentado um modelo de preservação da utilidade e da privacidade baseado em k-anonimização e "h-ceiling" um método que limita a generalização de dados. Na área da educação, Chicaiza et al (2020) apresenta um estudo sobre análise de dados de aprendizagem usando k-anonimato e modelos de regressão linear para avaliar a utilidade dos dados. Em Santos et al (2020) a utilidade de dados educacionais k-anonimizados é analisada calculando estatísticas descritivas para vários valores de k. Estudos recentes introduzem modelos de aprendizagem automática para garantir a privacidade dos dados e avaliar a sua utilidade (Eicher et al, 2020;Esquivel-Quirós et al, 2019).…”

Section: Trabalho Relacionadounclassified

Garantia de Privacidade Versus Utilidade dos Dados em Anonimização: um estudo no ensino superior

Prata

Ferrão²,

Santos

et al. 2020

risti

View full text Add to dashboard Cite

No mundo digital, toda a atividade humana deixa um rasto de dados que constitui um recurso cada vez mais valioso, para avaliação e definição de estratégias nos mais variados domínios. A partilha desses dados, sendo socialmente importante, implica o respeito pela privacidade individual e portanto a sua anonimização. As atuais leis e regulamentos sobre privacidade oferecem orientações limitadas para lidar com um vasto leque de tipos de dados, ou com técnicas de reidentificação. Este trabalho pretende ilustrar um processo de anonimização, comparando para vários modelos de privacidade a perda de informação e a utilidade do conjunto de dados resultante. Encontrar o equilíbrio entre privacidade e utilidade é um desafio que pode ser mais facilmente alcançado por quem melhor conhece o significado dos dados e dos objetivos que se pretendem alcançar com eles.

show abstract

“…Data privacy management is an essential part of ethical LA practice. There is a growing base of research on the measurement and mitigation of privacy risks to address ethical challenges presented by the collection and use of learner data for analytics (Chicaiza et al., 2020; Corrin et al., 2019; Drachsler & Greller, 2016; Ferguson, 2019; Gursoy et al., 2017; Hoel & Chen, 2016; Khalil & Ebner, 2016; Machado et al., 2019; Pardo & Siemens, 2014; Steiner et al., 2016). There is also some work addressing the cross‐disciplinary nature of learning analytics, including privacy concerns (eg, Teasley, 2019).…”

Section: Introductionmentioning

confidence: 99%

Implementing next generation privacy and ethics research in education technology

Marshall¹,

Pardo

Smith

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global “Ethical AI” movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. What is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters. Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries. A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics. What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics. It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline; It describes a repeatable framework for conducting ethical Learning Analytics. Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm. The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated. The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in earl...

show abstract

Application of data anonymization in Learning Analytics

Cited by 6 publications

References 7 publications

An End-to-End Data Pipeline for Managing Learning Analytics

An End-to-End Data Pipeline for Managing Learning Analytics

Garantia de Privacidade Versus Utilidade dos Dados em Anonimização: um estudo no ensino superior

Implementing next generation privacy and ethics research in education technology

Contact Info

Product

Resources

About