The large-scale monitoring of computer users' software activities has become commonplace,
eg, for application telemetry, error reporting, or demographic profiling. This paper describes …

Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a
technology for crowdsourcing statistics from end-user client software, anonymously, with …

This paper presents Prio, a privacy-preserving system for the collection of aggregate
statistics. Each Prio client holds a private data value (eg, its current location), and a small set …

Differential privacy is becoming a gold standard notion of privacy; it offers a guaranteed
bound on loss of privacy due to release of query results, even under worst-case …

Given a set D of tuples defined on a domain Omega, we study differentially private
algorithms for constructing a histogram over Omega to approximate the tuple distribution in …

Privacy-preserving distributed machine learning becomes increasingly important due to the
recent rapid growth of data. This paper focuses on a class of regularized empirical risk …

With the deep penetration of the Internet and mobile devices, privacy preservation in the
local setting has become increasingly relevant. The local setting refers to the scenario where …

Private communication over the Internet remains a challenging problem. Even if messages
are encrypted, it is hard to deliver them without revealing metadata about which pairs of …

Large-scale collection of contextual information is often essential in order to gather statistics,
train machine learning models, and extract knowledge from data. The ability to do so in a …

Targeted advertising has transformed the marketing landscape for a wide variety of
businesses, by creating new opportunities for advertisers to reach prospective customers by …