In this paper, we tackle the problem of answering multi-dimensional range queries under
local differential privacy. There are three key technical challenges: capturing the correlations …

Differential privacy is a promising approach to formalizing privacy—that is, for writing down
what privacy means as a mathematical equation. This book is provides overview of …

This paper studies privacy in the context of complex decision support queries composed of
multiple conditions on different aggregate statistics combined using disjunction and …

With the dramatic improvements in both the capability to collect personal data and the
capability to analyze large amounts of data, increasingly sophisticated and personal insights …

Local differential privacy (LDP) has been established as a strong privacy standard for
collecting sensitive information from users. Although it has attracted much research attention …

Ciphertexts of an order-preserving encryption (OPE) scheme preserve the order of their
corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this …

An important direction of informatics is devoted to the protection of privacy of confidential
information while providing answers to aggregated queries that can be used for analysis of …

Answering range queries in the context of Local Differential Privacy (LDP) is a widely
studied problem in Online Analytical Processing (OLAP). Existing LDP solutions all assume …

DP-BandMF offers a powerful approach to differentially private machine learning, balancing
privacy amplification with noise correlation for optimal noise reduction. However, its …

Due to the ongoing deprecation of third-party cookies on mainstream browsers, the digital
advertising industry is facing novel challenges regarding how to operate artificial …