We are a collaborative research group working on the development and application of Quantitative Information Flow (QIF), a mathematical framework for reasoning about information leaks in secure systems.
Our name “topete” is the Portuguese word for “quiff”.
Curious about QIF? Have a look at our new What is QIF? dialogue.
We maintain a curated list of QIF projects, tools, and resources.
The project A robust and explainable framework based on QIF for assessing big data privacy risks won a 2021 Google Latin America Research Award!
The paper Flexible and scalable privacy assessment for very large datasets with an application to official governmental microdata was accepted at PETS (Privacy Enhancing Technologies Symposium) 2022.
The paper A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study was accepted at PETS 2022.
The paper Universal Optimality and Robust Utility Bounds for Metric Differential Privacy was accepted at CSF (Symposium on Computer Security Foundations) 2022.