Differential privacy and swapping: Examining de-identification's impact on minority representation and privacy preservation in the US census
There has been considerable controversy regarding the accuracy and privacy of de-
identification mechanisms used in the US Decennial Census. We theoretically and …
identification mechanisms used in the US Decennial Census. We theoretically and …
Between privacy and utility: On differential privacy in theory and practice
Differential privacy (DP) aims to confer data processing systems with inherent privacy
guarantees, offering strong protections for personal data. But DP's approach to privacy …
guarantees, offering strong protections for personal data. But DP's approach to privacy …
Does Label Differential Privacy Prevent Label Inference Attacks?
Label differential privacy (label-DP) is a popular framework for training private ML models on
datasets with public features and sensitive private labels. Despite its rigorous privacy …
datasets with public features and sensitive private labels. Despite its rigorous privacy …
Differential perspectives: Epistemic disconnects surrounding the US Census Bureau's use of differential privacy
Abstract When the US Census Bureau announced its intention to modernize its disclosure
avoidance procedures for the 2020 Census, it sparked a controversy that is still underway …
avoidance procedures for the 2020 Census, it sparked a controversy that is still underway …
[PDF][PDF] A linear reconstruction approach for attribute inference attacks against synthetic data
MSMS Annamalai, A Gadotti, L Rocher - 2024 - usenix.org
Recent advances in synthetic data generation (SDG) have been hailed as a solution to the
difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn …
difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn …
Comment: The essential role of policy evaluation for the 2020 census disclosure avoidance system
In" Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's
Use of Differential Privacy," boyd and Sarathy argue that empirical evaluations of the …
Use of Differential Privacy," boyd and Sarathy argue that empirical evaluations of the …
Synthetic Health Data: Real Ethical Promise and Peril
Researchers and practitioners are increasingly using machine‐generated synthetic data as
a tool for advancing health science and practice, by expanding access to health data while …
a tool for advancing health science and practice, by expanding access to health data while …
Advancing differential privacy: Where we are now and future directions for real-world deployment
In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …
Estimating racial disparities when race is not observed
The estimation of racial disparities in various fields is often hampered by the lack of
individuallevel racial information. In many cases, the law prohibits the collection of such …
individuallevel racial information. In many cases, the law prohibits the collection of such …
Provable membership inference privacy
In applications involving sensitive data, such as finance and healthcare, the necessity for
preserving data privacy can be a significant barrier to machine learning model development …
preserving data privacy can be a significant barrier to machine learning model development …