978 citations · 1.2k across the 16 of their papers we have counts for
18 papers
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…
Certification for Differentially Private Prediction in Gradient-Based Training
Matthew Wicker, Philip Sosnin, Igor Shilov +5
We study private prediction where differential privacy is achieved by adding noise to the outputs of a non-private model. Existing methods rely on noise proportional to the global…
The Mosaic Memory of Large Language Models
Igor Shilov, Matthieu Meeus, Yves-Alexandre de Montjoye
As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial. Memorization in LLMs is widely assumed to onl…
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Nataša Krčo, Florent Guépin, Matthieu Meeus +2
Synthetic data generators and machine learning models can memorize their training data, posing privacy concerns. Membership inference attacks (MIAs) are a standard method of estima…
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…
Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models
Matthieu Meeus, Shubham Jain, Marek Rei +1
With large language models (LLMs) poised to become embedded in our daily lives, questions are starting to be raised about the data they learned from. These questions range from pot…
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…
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,…
Detrimental Network Effects in Privacy: A Graph-theoretic Model for Node-based Intrusions
Florimond Houssiau, Piotr Sapiezynski, Laura Radaelli +2
Despite proportionality being one of the tenets of data protection laws, we currently lack a robust analytical framework to evaluate the reach of modern data collections and the ne…
Towards matching user mobility traces in large-scale datasets
Dániel Kondor, Behrooz Hashemian, Yves-Alexandre de Montjoye +1
The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching…
Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics
Giuseppe D'Acquisto, Josep Domingo-Ferrer, Panayiotis Kikiras +3
The extensive collection and processing of personal information in big data analytics has given rise to serious privacy concerns, related to wide scale electronic surveillance, pro…
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Bjarke Felbo, Pål Sundsøy, Alex 'Sandy' Pentland +2
Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce. Basic demographic information is however often absent fr…
D4D-Senegal: The Second Mobile Phone Data for Development Challenge
Yves-Alexandre de Montjoye, Zbigniew Smoreda, Romain Trinquart +2
The D4D-Senegal challenge is an open innovation data challenge on anonymous call patterns of Orange's mobile phone users in Senegal. The goal of the challenge is to help address so…
The performance of modularity maximization in practical contexts
Benjamin H. Good, Yves-Alexandre de Montjoye, Aaron Clauset
Although widely used in practice, the behavior and accuracy of the popular module identification technique called modularity maximization is not well understood in practical contex…