Correlation inference attacks against machine learning models
Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset. We first propose a model-less attack, where an adversary exploits the spherical parametrization of correlation matrices alone to make an informed guess. Second, we propose a model-based attack, where an adversary exploits black-box model access to infer the correlations using minimal and realistic assumptions. Third, we evaluate our attacks against logistic regression and multilayer perceptron models on three tabular datasets and show the models to leak correlations. We finally show how extracted correlations can be used as building blocks for attribute inference attacks and enable weaker adversaries. Our results raise fundamental questions on what a model does and should remember from its training set.
Code (1)
Tasks
AttributeBIG-bench Machine LearningDecision MakingInference AttackSynthetic Data GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. In such an ML-equipped inference at…
BIG-bench Machine LearningInference AttackAttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual…
AttributeBIG-bench Machine LearningHolistic risk assessment of inference attacks in machine learning
As machine learning expanding application, there are more and more unignorable privacy and safety issues. Especially inference attacks against Machine Learning models allow adversaries to infer sensitive information abou…
AttributeInference AttackMembership Inference AttackML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they…
AttributeBIG-bench Machine LearningInference AttackKnowledge Distillation+1Adversarial attacks against Bayesian forecasting dynamic models
The last decade has seen the rise of Adversarial Machine Learning (AML). This discipline studies how to manipulate data to fool inference engines, and how to protect those systems against such manipulation attacks. Exten…
regressionTime SeriesTime Series AnalysisTime Series Forecasting