Time-uniform self-normalized concentration for vector-valued processes
Self-normalized processes arise naturally in many statistical tasks. While self-normalized
concentration has been extensively studied for scalar-valued processes, there is less work …
concentration has been extensively studied for scalar-valued processes, there is less work …
Adaptive principal component regression with applications to panel data
Principal component regression (PCR) is a popular technique for fixed-design error-in-
variables regression, a generalization of the linear regression setting in which the observed …
variables regression, a generalization of the linear regression setting in which the observed …
Privacy guarantees for personal mobility data in humanitarian response
Personal mobility data from mobile phones and other sensors are increasingly used to
inform policymaking during pandemics, natural disasters, and other humanitarian crises …
inform policymaking during pandemics, natural disasters, and other humanitarian crises …
Adaptive privacy composition for accuracy-first mechanisms
Although there has been work to develop ex-post private mechanisms from Ligett et al.'17
and Whitehouse et al'22 that seeks to provide privacy guarantees subject to a target level of …
and Whitehouse et al'22 that seeks to provide privacy guarantees subject to a target level of …
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 …
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach
Data engineering often requires accuracy (utility) constraints on results, posing significant
challenges in designing differentially private (DP) mechanisms, particularly under stringent …
challenges in designing differentially private (DP) mechanisms, particularly under stringent …
Private Count Release: A Simple and Scalable Approach for Private Data Analytics
R Rogers - arxiv preprint arxiv:2403.05073, 2024 - arxiv.org
We present a data analytics system that ensures accurate counts can be released with
differential privacy and minimal onboarding effort while showing instances that outperform …
differential privacy and minimal onboarding effort while showing instances that outperform …
[PDF][PDF] Modern Martingale Methods: Theory and Applications
JA Whitehouse - 2024 - reports-archive.adm.cs.cmu.edu
Martingale concentration is at the heart of sequential statistical inference. Due to their time-
uniform concentration of measure properties, martingales allow researchers to perform …
uniform concentration of measure properties, martingales allow researchers to perform …
Differential privacy with fine-grained provenance: Opportunities and challenges
X He, S Zhang - Data Engineering, 2023 - sites.computer.org
Differential privacy (DP) offers a robust framework for protecting individual privacy when
analyzing data. However, the elegant abstractions used in DP theory do not always translate …
analyzing data. However, the elegant abstractions used in DP theory do not always translate …
Randomized Response with Gradual Release of Privacy Budget
M Pan - arxiv preprint arxiv:2401.13952, 2024 - arxiv.org
An algorithm is developed to gradually relax the Differential Privacy (DP) guarantee of a
randomized response. The output from each relaxation maintains the same probability …
randomized response. The output from each relaxation maintains the same probability …