87 citations · 92 across the 6 of their papers we have counts for
6 papers
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
David Sánchez, Najeeb Jebreel, Krishnamurty Muralidhar +2
The threat of reconstruction attacks has led the U.S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank s…
Database Reconstruction Is Not So Easy and Is Different from Reidentification
Krishnamurty Muralidhar, Josep Domingo-Ferrer
In recent years, it has been claimed that releasing accurate statistical information on a database is likely to allow its complete reconstruction. Differential privacy has been sug…
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Alberto Blanco-Justicia, David Sanchez, Josep Domingo-Ferrer +1
We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP…
A Re-examination of the Census Bureau Reconstruction and Reidentification Attack
Krish Muralidhar
Recent analysis by researchers at the U.S. Census Bureau claims that by reconstructing the tabular data released from the 2010 Census, it is possible to reconstruct the original da…
General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model
Josep Domingo-Ferrer, Krishnamurty Muralidhar, Maria Bras-Amorós
Anonymization for privacy-preserving data publishing, also known as statistical disclosure control (SDC), can be viewed under the lens of the permutation model. According to this m…
New Directions in Anonymization: Permutation Paradigm, Verifiability by Subjects and Intruders, Transparency to Users
Josep Domingo-Ferrer, Krishnamurty Muralidhar
There are currently two approaches to anonymization: "utility first" (use an anonymization method with suitable utility features, then empirically evaluate the disclosure risk and,…