# Vortrag

Modul:   STA671  Kolloquium über anwendungsorientierte Statisik

## An Ontology on Data Anonymization and Privacy Computing Approaches

Vortrag von Dr. Matthias Templ

Datum: 13.05.22  Zeit: 15.15 - 16.15  Raum: ETH HG G 19.1

This talk is a practical presentation that aims to give an overview and ontology of different concepts on how to handle confidential data. It is motivated by the "fact" that different communities have different views and opinions on anonymization likely without knowing and understanding each other. To put it bluntly, a computer scientist will likely propose a very different solution to an anonymization problem than a survey statistician, and some scientists (and companies) believe that synthetic data is the sanctuary and solution par excellence, others simply promote privacy-​preserving data processing, while national statistical offices generally tend to reject these concepts, etc. Given the various methodological developments in the field of sensitive data protection, a conceptual classification and comparison between different methods from different domains is missing. Specifically, the goal is thus to provide guidance to practitioners who do not have an overview of appropriate approaches for specific scenarios, whether it is differential privacy for interactive queries, $k$ anonymity methods and synthetic data generation for publishing data, or secure federated analytics for multi-​party computations without sharing the data itself. After the brief introduction of the most important anonymization concepts, an overview and ontology is provided on methods based on key criteria that describe a context for handling data in a privacy-​compliant manner that enables informed decisions in the face of many alternatives. Throughout this presentation, it is emphasized that there is no panacea and that – as always - context matters.