# Empowering Open Data Sharing for Social Good: A Privacy-Aware Approach

arXiv: https://arxiv.org/abs/2408.17378 · HTML: https://mlsanity.com/paper/2408.17378/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [SoK: Challenges in Tabular Membership Inference Attacks](https://mlsanity.com/paper/2601.15874/) (2026)
- [CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data](https://mlsanity.com/paper/2505.23879/) (2025)
- [SoK: Rethinking Membership Inference in Tabular Machine Learning](https://mlsanity.com/paper/2610.18486/) (2026)

## Abstract (arXiv)

The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have promoted the availability of daily cases to support research initiatives, fostering collaboration between organisations and making such data available to the public through open data platforms. Despite the several advantages of data sharing, one of the major concerns before releasing health data is its impact on individuals' privacy. Such a sharing process should be based on state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a data set related to Covid-19 cases in the second largest hospital in Portugal to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. Our goal is to demonstrate how knowledge exchange in multidisciplinary teams of healthcare practitioners, data privacy, and data science experts is crucial to co-developing strategies that ensure high utility of de-identified data.