31 citations · 181 across the 22 of their papers we have counts for
22 papers
Why3-py: A Tool for Formal Verification of Hypothesis Testing and Meta-Analysis in Python
Akira Tanaka, Yusuke Kawamoto
The reproducibility crisis in scientific research has received widespread recognition, thereby increasing the importance of meta-analyses that integrate statistical analyses from m…
StatWhy: Formal Verification Tool for Statistical Hypothesis Testing Programs
Yusuke Kawamoto, Kentaro Kobayashi, Kohei Suenaga
Statistical methods have been widely misused and misinterpreted in various scientific fields, raising significant concerns about the integrity of scientific research. To mitigate t…
Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy
Yusuke Kawamoto, Kazumasa Miyake, Koichi Konishi +1
In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based…
Formalizing Statistical Causality via Modal Logic
Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
We propose a formal language for describing and explaining statistical causality. Concretely, we define Statistical Causality Language (StaCL) for expressing causal effects and spe…
Sound and Relatively Complete Belief Hoare Logic for Statistical Hypothesis Testing Programs
Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, w…
Theme Aspect Argumentation Model for Handling Fallacies
Ryuta Arisaka, Ryoma Nakai, Yusuke Kawamoto +1
From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend o…
Information Leakage Games: Exploring Information as a Utility Function
Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto +1
A common goal in the areas of secure information flow and privacy is to build effective defenses against unwanted leakage of information. To this end, one must be able to reason ab…
TransMIA: Membership Inference Attacks Using Transfer Shadow Training
Seira Hidano, Takao Murakami, Yusuke Kawamoto
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 t…
Locality Sensitive Hashing with Extended Differential Privacy
Natasha Fernandes, Yusuke Kawamoto, Takao Murakami
Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while k…
An Epistemic Approach to the Formal Specification of Statistical Machine Learning
Yusuke Kawamoto
We propose an epistemic approach to formalizing statistical properties of machine learning. Specifically, we introduce a formal model for supervised learning based on a Kripke mode…
Statistical Epistemic Logic
Yusuke Kawamoto
We introduce a modal logic for describing statistical knowledge, which we call statistical epistemic logic. We propose a Kripke model dealing with probability distributions and sto…
Towards Logical Specification of Statistical Machine Learning
Yusuke Kawamoto
We introduce a logical approach to formalizing statistical properties of machine learning. Specifically, we propose a formal model for statistical classification based on a Kripke…
Local Distribution Obfuscation via Probability Coupling
Yusuke Kawamoto, Takao Murakami
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…
Local Obfuscation Mechanisms for Hiding Probability Distributions
Yusuke Kawamoto, Takao Murakami
We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probabili…
Hybrid Statistical Estimation of Mutual Information and its Application to Information Flow
Fabrizio Biondi, Yusuke Kawamoto, Axel Legay +1
Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhau…
On the Anonymization of Differentially Private Location Obfuscation
Yusuke Kawamoto, Takao Murakami
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 anonymi…
Utility-Optimized Local Differential Privacy Mechanisms for Distribution Estimation
Takao Murakami, Yusuke Kawamoto
LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal data (e.g., distribution underlying the data) while protecting users' privacy. Although…
Leakage and Protocol Composition in a Game-Theoretic Perspective
Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto +1
In the inference attacks studied in Quantitative Information Flow (QIF), the adversary typically tries to interfere with the system in the attempt to increase its leakage of secret…
A Game-Theoretic Approach to Information-Flow Control via Protocol Composition
Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto +1
In the inference attacks studied in Quantitative Information Flow (QIF), the attacker typically tries to interfere with the system in the attempt to increase its leakage of secret…
Information Leakage Games
Mário S. Alvim, Konstantinos Chatzikokolakis, Yusuke Kawamoto +1
We consider a game-theoretic setting to model the interplay between attacker and defender in the context of information flow, and to reason about their optimal strategies. In contr…
On the Compositionality of Quantitative Information Flow
Yusuke Kawamoto, Konstantinos Chatzikokolakis, Catuscia Palamidessi
Information flow is the branch of security that studies the leakage of information due to correlation between secrets and observables. Since in general such correlation cannot be a…
Quantitative Information Flow for Scheduler-Dependent Systems
Yusuke Kawamoto, Thomas Given-Wilson
Quantitative information flow analyses measure how much information on secrets is leaked by publicly observable outputs. One area of interest is to quantify and estimate the inform…