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Testing differential privacy with dual interpreters
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-78800-3_24openalex-snapshot
Z3: An Efficient SMT Solver
inferredcites →https://doi.org/10.1007/978-3-642-40447-4_2openalex-snapshot
Combining Deep and Shallow Embedding for EDSL
inferredcites →https://doi.org/10.1016/j.jeconom.2016.01.003openalex-snapshot
Multivariate and multiple permutation tests
inferredcites →https://doi.org/10.1080/01621459.1951.10500769openalex-snapshot
The Kolmogorov-Smirnov Test for Goodness of Fit
inferredcites →https://doi.org/10.1109/csf.2014.35openalex-snapshot
Differential Privacy: An Economic Method for Choosing Epsilon
inferredcites →https://doi.org/10.1109/lics.1989.39155openalex-snapshot
Computational lambda-calculus and monads
inferredcites →https://doi.org/10.1109/lics.2019.8785668openalex-snapshot
Approximate Span Liftings: Compositional Semantics for Relaxations of Differential Privacy
inferredcites →https://doi.org/10.1109/memocode16638.2010openalex-snapshot
Eighth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2010)
inferredcites →https://doi.org/10.1145/1536414.1536464openalex-snapshot
Universally utility-maximizing privacy mechanisms
inferredcites →https://doi.org/10.1145/1863543.1863568openalex-snapshot
Distance makes the types grow stronger
inferredcites →https://doi.org/10.1145/2382196.2382264openalex-snapshot
On significance of the least significant bits for differential privacy
inferredcites →https://doi.org/10.1145/2480359.2429113openalex-snapshot
Linear dependent types for differential privacy
inferredcites →https://doi.org/10.1145/2666356.2594340openalex-snapshot
A lightweight symbolic virtual machine for solver-aided host languages
inferredcites →https://doi.org/10.1145/2933575.2934554openalex-snapshot
Proving Differential Privacy via Probabilistic Couplings
inferredcites →https://doi.org/10.1145/2976749.2978391openalex-snapshot
Advanced Probabilistic Couplings for Differential Privacy
inferredcites →https://doi.org/10.1145/3009837.3009884openalex-snapshot
LightDP: towards automating differential privacy proofs
inferredcites →https://doi.org/10.1145/3093333.3009884openalex-snapshot
LightDP: towards automating differential privacy proofs
inferredcites →https://doi.org/10.1145/3110254openalex-snapshot
A framework for adaptive differential privacy
inferredcites →https://doi.org/10.1145/3158146openalex-snapshot
Synthesizing coupling proofs of differential privacy
inferredcites →https://doi.org/10.1145/3243734.3243818openalex-snapshot
Detecting Violations of Differential Privacy
inferredcites →https://doi.org/10.1145/3314221.3314619openalex-snapshot
Proving differential privacy with shadow execution
inferredcites →https://doi.org/10.1145/3354166.3354175openalex-snapshot
Relational Symbolic Execution
inferredcites →https://doi.org/10.1145/3360598openalex-snapshot
Duet: an expressive higher-order language and linear type system for statically enforcing differential privacy
inferredcites →https://doi.org/10.1145/360248.360252openalex-snapshot
Symbolic execution and program testing
inferredcites →https://doi.org/10.1198/000313008x332421openalex-snapshot
P-Values are Random Variables
inferredcites →https://doi.org/10.12688/gatesopenres.13089.1openalex-snapshot
Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff
inferredcites →https://doi.org/10.14778/3055330.3055331openalex-snapshot
Understanding the sparse vector technique for differential privacy
inferredcites →https://doi.org/10.1561/0400000042openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.29012/jpc.v6i1.634openalex-snapshot
On the 'Semantics' of Differential Privacy: A Bayesian Formulation
inferredcites →https://doi.org/10.52202/085713openalex-snapshot
Advances in Neural Information Processing Systems 19
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-72019-3_8openalex-snapshot
Coupled Relational Symbolic Execution for Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-3-030-94583-1_9openalex-snapshot
Verifying Pufferfish Privacy in Hidden Markov Models
inferred← citeshttps://doi.org/10.1007/978-3-032-40374-2_11openalex-snapshot
Verifying $$\varepsilon $$-Differential Privacy Bit by Bit
inferred← citeshttps://doi.org/10.1109/csf54842.2022.9919653openalex-snapshot
The Complexity of Verifying Boolean Programs as Differentially Private
inferred← citeshttps://doi.org/10.1109/csf61375.2024.00048openalex-snapshot
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00107openalex-snapshot
Testing and Understanding Deviation Behaviors in FHE-Hardened Machine Learning Models
inferred← citeshttps://doi.org/10.1109/sp40001.2021.00081openalex-snapshot
DP-Sniper: Black-Box Discovery of Differential Privacy Violations using Classifiers
inferred← citeshttps://doi.org/10.1109/sp46214.2022.9833689openalex-snapshot
Statistical Quantification of Differential Privacy: A Local Approach
inferred← citeshttps://doi.org/10.1109/sp54263.2024.00166openalex-snapshot
Eureka: A General Framework for Black-box Differential Privacy Estimators
inferred← citeshttps://doi.org/10.1109/sp61157.2025.00181openalex-snapshot
Is MPC Secure? Leveraging Neural Network Classifiers to Detect Data Leakage Vulnerabilities in MPC Implementations
inferred← citeshttps://doi.org/10.1145/3589207openalex-snapshot
Contextual Linear Types for Differential Privacy
inferred← citeshttps://doi.org/10.1145/3729294openalex-snapshot
Verified Foundations for Differential Privacy
inferred← citeshttps://doi.org/10.14722/ndss.2024.23380openalex-snapshot
MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models
inferred← citeshttps://doi.org/10.48550/arxiv.2109.10789openalex-snapshot
Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries
inferred← citeshttps://doi.org/10.56553/popets-2022-0070openalex-snapshot
Privacy accounting εconomics: Improving differential privacy composition via a posteriori bounds
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