LOGAN: Membership Inference Attacks Against Generative Models

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Defense-gan: Protecting Classifiers against Adversarial Attacks Using Generative Models

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend de...

متن کامل

LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks

Recent advances in machine learning are paving the way for the artificial generation of high quality images and videos. In this paper, we investigate how generating synthetic samples through generative models can lead to information leakage, and, consequently, to privacy breaches affecting individuals’ privacy that contribute their personal or sensitive data to train these models. In order to q...

متن کامل

Scalable Inference in Hierarchical Generative Models

Borrowing insights from computational neuroscience, we present a family of inference algorithms for a class of generative statistical models specifically designed to run on commonly-available distributed-computing hardware. The class of generative models is roughly based on the architecture of the visual cortex and shares some of the same structural and computational characteristics. In additio...

متن کامل

Asymptotically exact inference in differentiable generative models

Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density. This framework includes both deep generative architectures such as Variational Autoencoders and a large class of procedurally defined simulator models. We present a method for performing efficient MCMC inference in such models when conditioning on observations of the...

متن کامل

Identifying inference attacks against healthcare data repositories

Health care data repositories play an important role in driving progress in medical research. Finding new pathways to discovery requires having adequate data and relevant analysis. However, it is critical to ensure the privacy and security of the stored data. In this paper, we identify a dangerous inference attack against naive suppression based approaches that are used to protect sensitive inf...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Proceedings on Privacy Enhancing Technologies

سال: 2018

ISSN: 2299-0984

DOI: 10.2478/popets-2019-0008