Differential privacy has become an integral way for data scientists to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual’s data to be distinguished or re-identified. To help more researchers with their work, IBM released the open-source Differential Privacy Library. The library “boasts a suite of tools for machine learning and data analytics tasks, all with built-in privacy guarantees,” according to Naoise Holohan, a research staff member on IBM Research Europe’s privacy and security team.
What is it that data analysts want? Acknowledging that data quality is a subjective concept, we develop a framework to evaluate the quality of differentially private synthetic data from an applied researcher’s perspective. Data quality can be measured along two dimensions. First, quality of synthetic data can be evaluated against training data or against an underlying population. Second, the quality of synthetic data depends on general similarity of distributions or specific tasks such as inference or prediction. It is clear that accommodating all goals at once is a formidable challenge. We invite the academic community to jointly advance the privacy-quality frontier.
Relatório de resultados do projeto Data, Privacy and the Individual do Center for the Governance of Change, IE University. O relatório contém um resumo de sete artigos e um questionário, resultados do projeto. Os temas vão da ética do tratamento de dados pessoais aos meios técnicos e tecnológicos de garantia da privacidade. A seguir um breve resumo em tópicos dos principais pontos levantados, e o link para download do documento ao final.Continuar a ler “[relatório] Data, privacy and the individual”