Ashwin Machanavajjhala

research

∙ 09/15/2023

DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms

Synthetic data generation methods, and in particular, private synthetic ...

0 Shweta Patwa, et al. ∙

research

∙ 08/30/2023

Publishing Wikipedia usage data with strong privacy guarantees

For almost 20 years, the Wikimedia Foundation has been publishing statis...

0 Temilola Adeleye, et al. ∙

research

∙ 12/20/2022

PreFair: Privately Generating Justifiably Fair Synthetic Data

When a database is protected by Differential Privacy (DP), its usability...

0 David Pujol, et al. ∙

research

∙ 12/19/2022

Multi-Analyst Differential Privacy for Online Query Answering

Most differentially private mechanisms are designed for the use of a sin...

0 David Pujol, et al. ∙

research

∙ 12/08/2022

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

In this short paper, we outline the design of Tumult Analytics, a Python...

0 Skye Berghel, et al. ∙

research

∙ 09/07/2022

Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census

The purpose of this paper is to guide interpretation of the semantic pri...

0 Daniel Kifer, et al. ∙

research

∙ 09/02/2022

DPXPlain: Privately Explaining Aggregate Query Answers

Differential privacy (DP) is the state-of-the-art and rigorous notion of...

0 Yuchao Tao, et al. ∙

research

∙ 04/19/2022

The 2020 Census Disclosure Avoidance System TopDown Algorithm

The Census TopDown Algorithm (TDA) is a disclosure avoidance system usin...

0 John M. Abowd, et al. ∙

research

∙ 03/09/2022

IncShrink: Architecting Efficient Outsourced Databases using Incremental MPC and Differential Privacy

In this paper, we consider secure outsourced growing databases that supp...

0 Chenghong Wang, et al. ∙

research

∙ 12/16/2021

Benchmarking Differentially Private Synthetic Data Generation Algorithms

This work presents a systematic benchmark of differentially private synt...

0 Yuchao Tao, et al. ∙

research

∙ 11/08/2021

Equity and Privacy: More Than Just a Tradeoff

While the entire field of privacy preserving data analytics is focused o...

0 David Pujol, et al. ∙

research

∙ 07/22/2021

Differentially Private Algorithms for 2020 Census Detailed DHC Race & Ethnicity

This article describes a proposed differentially private (DP) algorithms...

0 Sam Haney, et al. ∙

research

∙ 06/23/2021

HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a...

0 Ryan McKenna, et al. ∙

research

∙ 06/09/2021

Prior-Aware Distribution Estimation for Differential Privacy

Joint distribution estimation of a dataset under differential privacy is...

0 Yuchao Tao, et al. ∙

research

∙ 03/29/2021

DP-Sync: Hiding Update Patterns in Secure Outsourced Databases with Differential Privacy

In this paper, we have introduced a new type of leakage associated with ...

0 Chenghong Wang, et al. ∙

research

∙ 03/26/2021

Synthesizing Linked Data Under Cardinality and Integrity Constraints

The generation of synthetic data is useful in multiple aspects, from tes...

0 Amir Gilad, et al. ∙

research

∙ 11/02/2020

Budget Sharing for Multi-Analyst Differential Privacy

Large organizations that collect data about populations (like the US Cen...

0 David Pujol, et al. ∙

research

∙ 04/19/2020

DP-Cryptography: Marrying Differential Privacy and Cryptography in Emerging Applications

Differential privacy (DP) has arisen as the state-of-the-art metric for ...

0 Sameer Wagh, et al. ∙

research

∙ 04/09/2020

Computing Local Sensitivities of Counting Queries with Joins

Local sensitivity of a query Q given a database instance D, i.e. how muc...

0 Yuchao Tao, et al. ∙

research

∙ 08/27/2019

Answering Summation Queries for Numerical Attributes under Differential Privacy

In this work we explore the problem of answering a set of sum queries un...

0 Yikai Wu, et al. ∙

research

∙ 07/03/2019

Capacity Bounded Differential Privacy

Differential privacy, a notion of algorithmic stability, is a gold stand...

0 Kamalika Chaudhuri, et al. ∙

research

∙ 05/29/2019

Fair Decision Making using Privacy-Protected Data

Data collected about individuals is regularly used to make decisions tha...

0 Satya Kuppam, et al. ∙

research

∙ 02/20/2019

Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

Differential privacy has steadily become the de-facto standard for achie...

0 Amrita Roy Chowdhury, et al. ∙

research

∙ 10/03/2018

Shrinkwrap: Differentially-Private Query Processing in Private Data Federations

A private data federation is a set of autonomous databases that share a ...

0 Johes Bater, et al. ∙

research

∙ 08/10/2018

Ektelo: A Framework for Defining Differentially-Private Computations

The adoption of differential privacy is growing but the complexity of de...

0 Dan Zhang, et al. ∙

research

∙ 08/10/2018

Optimizing error of high-dimensional statistical queries under differential privacy

Differentially private algorithms for answering sets of predicate counti...

0 Ryan McKenna, et al. ∙

research

∙ 04/02/2018

Differentially Private Hierarchical Count-of-Counts Histograms

We consider the problem of privately releasing a class of queries that w...

0 Yu-Hsuan Kuo, et al. ∙

research

∙ 04/02/2018

Differentially Private Hierarchical Group Size Estimation

Consider the problem of estimating, for every integer j, the number of h...

0 Yu-Hsuan Kuo, et al. ∙

research

∙ 12/29/2017

Private Exploration Primitives for Data Cleaning

Data cleaning is the process of detecting and repairing inaccurate or co...

0 Chang Ge, et al. ∙

research

∙ 12/16/2017

One-sided Differential Privacy

In this paper, we study the problem of privacy-preserving data sharing, ...

0 Stelios Doudalis, et al. ∙

Ashwin Machanavajjhala

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