CISPA
Browse

File(s) not publicly available

Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

conference contribution
posted on 2023-11-29, 18:19 authored by Rakibul Hasan, Mario FritzMario Fritz
Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (eg, targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.

History

Preferred Citation

Rakibul Hasan and Mario Fritz. Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring. In: Privacy Enhancing Technologies Symposium (PETS). 2022.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

Privacy Enhancing Technologies Symposium (PETS)

Legacy Posted Date

2022-03-11

Open Access Type

  • Gold

BibTeX

@inproceedings{cispa_all_3582, title = "Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring", author = "Hasan, Rakibul and Fritz, Mario", booktitle="{Privacy Enhancing Technologies Symposium (PETS)}", year="2022", }

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC