14 citations · 31 across the 12 of their papers we have counts for
12 papers
SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)
William Sexton, Skye Berghel, Bayard Carlson +10
This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detai…
SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)
Sam Haney, Skye Berghel, Bayard Carlson +11
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) o…
PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)
William Sexton, Skye Berghel, Bayard Carlson +10
This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File (S-D…
Slowly Scaling Per-Record Differential Privacy
Brian Finley, Anthony M Caruso, Justin C Doty +5
We develop formal privacy mechanisms for releasing statistics from data with many outlying values, such as income data. These mechanisms ensure that a per-record differential priva…
Privately Answering Queries on Skewed Data via Per Record Differential Privacy
Jeremy Seeman, William Sexton, David Pujol +1
We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large en…
Publishing Wikipedia usage data with strong privacy guarantees
Temilola Adeleye, Skye Berghel, Damien Desfontaines +9
For almost 20 years, the Wikimedia Foundation has been publishing statistics about how many people visited each Wikipedia page on each day. This data helps Wikipedia editors determ…
Multi-Analyst Differential Privacy for Online Query Answering
David Pujol, Albert Sun, Brandon Fain +1
Most differentially private mechanisms are designed for the use of a single analyst. In reality, however, there are often multiple stakeholders with different and possibly conflict…
PreFair: Privately Generating Justifiably Fair Synthetic Data
David Pujol, Amir Gilad, Ashwin Machanavajjhala
When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the propertie…
Equity and Privacy: More Than Just a Tradeoff
David Pujol, Ashwin Machanavajjhala
While the entire field of privacy preserving data analytics is focused on the privacy-utility tradeoff, recent work has shown that privacy preserving data publishing can introduce…
Budget Sharing for Multi-Analyst Differential Privacy
David Pujol, Yikai Wu, Brandon Fain +1
Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and po…
Answering Summation Queries for Numerical Attributes under Differential Privacy
Yikai Wu, David Pujol, Ios Kotsogiannis +1
In this work we explore the problem of answering a set of sum queries under Differential Privacy. This is a little understood, non-trivial problem especially in the case of numeric…
Fair Decision Making using Privacy-Protected Data
Satya Kuppam, Ryan Mckenna, David Pujol +3
Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who wi…