14 citations · 37 across the 10 of their papers we have counts for
12 papers
Evaluating Concept Filtering Defenses against Child Sexual Abuse Material Generation by Text-to-Image Models
Ana-Maria Cretu, Klim Kireev, Amro Abdalla +5
We evaluate the effectiveness of filtering child images from training datasets of text-to-image models to prevent model misuse to create child sexual abuse material (CSAM). First,…
A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
Klim Kireev, Ana-Maria Creţu, Raphael Meier +3
Platforms and the law regulate digital content depicting minors (defined as individuals under 18 years of age) differently from other types of content. Given the sheer amount of co…
QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems
Bozhidar Stevanoski, Ana-Maria Cretu, Yves-Alexandre de Montjoye
Query-based systems (QBSs) are one of the key approaches for sharing data. QBSs allow analysts to request aggregate information from a private protected dataset. Attacks are a cruc…
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
Vincent Guan, Florent Guépin, Ana-Maria Cretu +1
Location data is frequently collected from populations and shared in aggregate form to guide policy and decision making. However, the prevalence of aggregated data also raises the…
Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks
Ana-Maria Cretu, Miruna Rusu, Yves-Alexandre de Montjoye
Smart meters, devices measuring the electricity and gas consumption of a household, are currently being deployed at a fast rate throughout the world. The data they collect are extr…
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data
Florent Guépin, Matthieu Meeus, Ana-Maria Cretu +1
Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on sha…
Deep perceptual hashing algorithms with hidden dual purpose: when client-side scanning does facial recognition
Shubham Jain, Ana-Maria Cretu, Antoine Cully +1
End-to-end encryption (E2EE) provides strong technical protections to individuals from interferences. Governments and law enforcement agencies around the world have however raised…
Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing
Matthieu Meeus, Florent Guépin, Ana-Maria Cretu +1
Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become…
Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting
Ana-Maria Cretu, Daniel Jones, Yves-Alexandre de Montjoye +1
Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box ac…
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems
Ana-Maria Cretu, Florimond Houssiau, Antoine Cully +1
Although query-based systems (QBS) have become one of the main solutions to share data anonymously, building QBSes that robustly protect the privacy of individuals contributing to…
Correlation inference attacks against machine learning models
Ana-Maria Creţu, Florent Guépin, Yves-Alexandre de Montjoye
Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks,…
Diagnosing Disagreements: The Authentication of the Positron 1931-1934
Ana-Maria Cretu
This paper bridges a historiographical gap in accounts of the prediction and discovery of the positron by combining three ingredients. First, the prediction and discovery of the po…