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Found 7 papers, 5 papers with code
An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions
Noisy marginals are a common form of confidentiality-protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation.
Answering Private Linear Queries Adaptively using the Common Mechanism
Since M* will be used no matter what, the analyst can use its output to decide whether to subsequently run M1'(thus recreating the analysis supported by M1) or M2'(recreating the analysis supported by M2), without wasting privacy loss budget.
Optimizing Fitness-For-Use of Differentially Private Linear Queries
In practice, differentially private data releases are designed to support a variety of applications.
Databases
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
We propose CheckDP, the first automated and integrated approach for proving or disproving claims that a mechanism is differentially private.
Programming Languages D.3.1
Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms
We show that it can also release for free the noisy gap between the approximate maximizer and runner-up.
Proving Differential Privacy with Shadow Execution
Sometimes, combining those two requires substantial changes to program logics: one recent paper is able to verify Report Noisy Max automatically, but it involves a complex verification system using customized program logics and verifiers.
Programming Languages D.2.4
Toward Detecting Violations of Differential Privacy
The widespread acceptance of differential privacy has led to the publication of many sophisticated algorithms for protecting privacy.
Cryptography and Security
