activity
20222024
most citedFlexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata

15 citations · 39 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2024★ 9 cited

The Privacy-Utility Trade-off in the Topics API

Mário S. Alvim, Natasha Fernandes, Annabelle McIver +1

The ongoing deprecation of third-party cookies by web browser vendors has sparked the proposal of alternative methods to support more privacy-preserving personalized advertising on…

cs.CR2023★ 7 cited

A Quantitative Information Flow Analysis of the Topics API

Mário S. Alvim, Natasha Fernandes, Annabelle McIver +1

Third-party cookies have been a privacy concern since cookies were first developed in the mid 1990s, but more strict cookie policies were only introduced by Internet browser vendor…

cs.CR2023★ 8 cited

A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow

Mário S. Alvim, Natasha Fernandes, Annabelle McIver +2

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The com…

cs.CR2023

Measuring Re-identification Risk

CJ Carey, Travis Dick, Alessandro Epasto +8

Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification r…

cs.CR2022★ 15 cited

Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata

Mário S. Alvim, Natasha Fernandes, Annabelle McIver +2

We present a systematic refactoring of the conventional treatment of privacy analyses, basing it on mathematical concepts from the framework of Quantitative Information Flow (QIF).…