15 citations · 56 across the 19 of their papers we have counts for
20 papers
Goodbye Equal Error Rate, Hello Local Information Disclosure: Evaluating Voice Anonymisation against 1-to-N Linkage Threats
Dāvis Šterns, Konstantinos Drossos, Natasha Fernandes +2
Voice anonymisation aims to protect speaker identity. Currently, its empirical privacy evaluation heavily relies on the Equal Error Rate (EER). Originally designed for biometric ve…
Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
Ramon G. Gonze, Natasha Fernandes, Heber H. Arcolezi +2
Local Differential Privacy (LDP) has become the de facto standard for privacy-preserving data collection in large-scale systems, in particular for the purpose of estimating frequen…
Composition Theorems for f-Differential Privacy
Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi
"f differential privacy" (fDP) is a recent definition for privacy privacy which can offer improved predictions of "privacy loss". It has been used to analyse specific privacy mecha…
What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen +3
While location trajectories offer valuable insights, they also reveal sensitive personal information. Differential Privacy (DP) offers formal protection, but achieving a favourable…
Empirical Calibration and Metric Differential Privacy in Language Models
Pedro Faustini, Natasha Fernandes, Annabelle McIver +1
NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the privacy budget . However, doe…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
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…
Bayes' capacity as a measure for reconstruction attacks in federated learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy…
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…
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…
Directional Privacy for Deep Learning
Pedro Faustini, Natasha Fernandes, Shakila Tonni +2
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. It applies isotropic Gaussian noise to gra…
Explaining epsilon in local differential privacy through the lens of quantitative information flow
Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi
The study of leakage measures for privacy has been a subject of intensive research and is an important aspect of understanding how privacy leaks occur in computer systems. Differen…
A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study
Danilo Fabrino Favato, Gabriel Coutinho, Mário S. Alvim +1
In this paper we describe, formalize, implement, and experimentally evaluate a novel transaction re-identification attack against official foreign-trade statistics releases in Braz…
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy
Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi +1
We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the best mechanism fo…
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).…
The Laplace Mechanism has optimal utility for differential privacy over continuous queries
Natasha Fernandes, Annabelle McIver, Carroll Morgan
Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the r…
Locality Sensitive Hashing with Extended Differential Privacy
Natasha Fernandes, Yusuke Kawamoto, Takao Murakami
Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while k…
Utility-Preserving Privacy Mechanisms for Counting Queries
Natasha Fernandes, Kacem Lefki, Catuscia Palamidessi
Differential privacy (DP) and local differential privacy (LPD) are frameworks to protect sensitive information in data collections. They are both based on obfuscation. In DP the no…
Generalised Differential Privacy for Text Document Processing
Natasha Fernandes, Mark Dras, Annabelle McIver
We address the problem of how to "obfuscate" texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text. In th…
Author Obfuscation Using Generalised Differential Privacy
Natasha Fernandes, Mark Dras, Annabelle McIver
The problem of obfuscating the authorship of a text document has received little attention in the literature to date. Current approaches are ad-hoc and rely on assumptions about an…