Anonymizing private data before release is not enough to reliably protect privacy, as Netflix
and AOL have learned to their cost. Recent research on differential privacy opens a way to …

This paper presents Orchard, a system that can answer queries about sensitive data that is
held by millions of user devices, with strong differential privacy guarantees. Orchard …

Recently, a number of systems have been deployed that gather sensitive statistics from user
devices while giving differential privacy guarantees. One prominent example is the …

Differential privacy is an information theoretic constraint on algorithms and code. It provides
quantification of privacy leakage and formal privacy guarantees that are currently …

We propose a hybrid model of differential privacy that considers a combination of regular
and opt-in users who desire the differential privacy guarantees of the local privacy model …

Local differential privacy (LDP), where users randomly perturb their inputs to provide
plausible deniability of their data without the need for a trusted party, has been adopted …

We study the problem of privacy-preserving data sharing, wherein only a subset of the
records in a database is sensitive, possibly based on predefined privacy policies. Existing …

LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal
data (eg, distribution underlying the data) while protecting users' privacy. Although LDP …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

Computing aggregate statistics about user data is of vital importance for a variety of services
and systems, but this practice has been shown to seriously undermine the privacy of users …