Differential privacy comes equipped with multiple analytical tools for the design of private
data analyses. One important tool is the so-called" privacy amplification by subsampling" …

This work studies differential privacy in the context of the recently proposed shuffle model.
Unlike in the local model, where the server collecting privatized data from users can track …

In June 2016, Apple announced that it will deploy differential privacy for some user data
collection in order to ensure privacy of user data, even from Apple. The details of Apple's …

The widespread acceptance of differential privacy has led to the publication of many
sophisticated algorithms for protecting privacy. However, due to the subtle nature of this …

Integrating cognitive radio (CR) with traditional wireless networks is helping solve the
problem of spectrum scarcity in an efficient manner. The opportunistic and dynamic …

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 …

Quantum algorithms for solving a wide range of practical problems have been proposed in
the last ten years, such as data search and analysis, product recommendation, and credit …

We consider the quantitative problem of obtaining lower-bounds on the probability of
termination of a given non-deterministic probabilistic program. Specifically, given a non …

We study the fundamental problems of identity testing (goodness of fit), and closeness
testing (two sample test) of distributions over $ k $ elements, under differential privacy. While …

Probabilistic programs extend classical imperative programs with real-valued random
variables and random branching. The most basic liveness property for such programs is the …