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Found 13 papers, 1 papers with code
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy
In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based (ML-based) systems.
Formalizing Statistical Causality via Modal Logic
We propose a formal language for describing and explaining statistical causality.
Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs
We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately.
Theme Aspect Argumentation Model for Handling Fallacies
We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability.
Information Leakage Games: Exploring Information as a Utility Function
A common goal in the areas of secure information flow and privacy is to build effective defenses against unwanted leakage of information.
TransMIA: Membership Inference Attacks Using Transfer Shadow Training
Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different training.
Locality Sensitive Hashing with Extended Differential Privacy
Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.
An Epistemic Approach to the Formal Specification of Statistical Machine Learning
We propose an epistemic approach to formalizing statistical properties of machine learning.
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features
We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor.
Towards Logical Specification of Statistical Machine Learning
Specifically, we propose a formal model for statistical classification based on a Kripke model, and formalize various notions of classification performance, robustness, and fairness of classifiers by using epistemic logic.
Local Distribution Obfuscation via Probability Coupling
We introduce a general model for the local obfuscation of probability distributions by probabilistic perturbation, e. g., by adding differentially private noise, and investigate its theoretical properties.
Local Obfuscation Mechanisms for Hiding Probability Distributions
To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output.
Cryptography and Security Databases Information Theory Information Theory
On the Anonymization of Differentially Private Location Obfuscation
Obfuscation techniques in location-based services (LBSs) have been shown useful to hide the concrete locations of service users, whereas they do not necessarily provide the anonymity.
Cryptography and Security Databases Information Theory Information Theory
