Publications by Ana-Maria Cretu

Related publications (6)

Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting

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 access to the model parameters increases the attack surface compared to black- ...

Privacy Enhancing Technologies Symposium Advisory Board2024

Anonymization: The imperfect science of using data while preserving privacy

Ana-Maria Cretu

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such d ...

AMER ASSOC ADVANCEMENT SCIENCE2024

Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks

Ana-Maria Cretu

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 extremely useful, including in the fight against climate change. However, these ...

Association for Computing Machinery, Inc2024

A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data

Ana-Maria Cretu

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 privacy concern of membership inference attacks (MIAs). MIAs infer whether a ...

Privacy Enhancing Technologies Symposium Advisory Board2024

Correlation inference attacks against machine learning models

Ana-Maria Cretu

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, whether and when a model leaks information about the correlations between t ...

2024

QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems

Ana-Maria Cretu

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 crucial part of ensuring QBSs are truly privacy-preserving. The development and ...

Association for Computing Machinery, Inc2024