Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

We introduce the notion of a reproducible algorithm in the context of learning. A reproducible
learning algorithm is resilient to variations in its samples—with high probability, it returns the …

We present DP-Sniper, a practical black-box method that automatically finds violations of
differential privacy. DP-Sniper is based on two key ideas:(i) training a classifier to predict if …

(ε, δ) differential privacy has seen increased adoption recently, especially in private machine
learning applications. While this privacy definition allows provably limiting the amount of …

Corporations, organizations, and governments have collected, digitized, and stored
information in digital forms since the invention of computers, and the speed of such data …

Over the last decade, differential privacy has achieved widespread adoption within the
privacy community. Moreover, it has attracted significant attention from the verification …

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 …

The growing popularity and adoption of differential privacy in academic and industrial
settings has resulted in the development of increasingly sophisticated algorithms for …

Establishing quantitative bounds on the execution cost of programs is essential in many
areas of computer science such as complexity analysis, compiler optimizations, security and …

Multi-core chips have been increasingly adopted by the microprocessor industry. For real-
time systems to exploit multi-core architectures, it is required to obtain both tight and safe …