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 …

Bounding privacy leakage over compositions, ie, privacy accounting, is a key challenge in
differential privacy (DP). However, the privacy parameter ($\varepsilon $ or $\delta $) is often …

Differential privacy is a de facto standard for statistical computations over databases that
contain private data. Its main and rather surprising strength is to guarantee individual privacy …

Majority-SAT (aka MAJ-SAT) is the problem of determining whether an input n-variable
formula in conjunctive normal form (CNF) has at least 2^(n-1) satisfying assignments …

In past years, entire research communities have arisen to address concerns of privacy and
fairness in data analysis. At present, however, the public must trust that institutions will re …

We study the complexity of the problem of verifying differential privacy for while-like
programs working over boolean values and making probabilistic choices. Programs in this …

We consider the problem of checking the differential privacy of online randomized
algorithms that process a stream of inputs and produce outputs corresponding to each input …

Programmatically generating tight differential privacy (DP) bounds is a hard problem. Two
core challenges are (1) finding expressive, compact, and efficient encodings of the …

Pufferfish is a Bayesian privacy framework for designing and analyzing privacy mechanisms.
It refines differential privacy, the current gold standard in data privacy, by allowing explicit …

We introduce an automata model for describing interesting classes of differential privacy
mechanisms/algorithms that include known mechanisms from the literature. These automata …