{"repo":"privacytrustlab/ml_privacy_meter","free":true,"listed":false,"github":"https://github.com/privacytrustlab/ml_privacy_meter","clone":"git clone https://github.com/privacytrustlab/ml_privacy_meter.git","description":"Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.","language":"Jupyter Notebook","stars":723,"topics":["machine-learning","data-privacy","privacy","inference","information-leakage","gdpr","data-protection","data-protection-impact-assessment","privacy-audit","explainable-ai"],"license":"MIT","category":"machine-learning","readme_excerpt":"Privacy Meter What is Privacy Meter? Privacy Meter is an open-source library to audit data privacy in a wide range of statistical and machine learning algorithms (classification, regression, computer vision, and natural language processing). The tool enables data protection impact assessment based on the state-of-the-art membership inference attacks. Why Privacy Meter? Machine learning is playing a central role in automated decision-making in a wide range of organizations and service providers. The data, which are used to train the models, typically contain sensitive information about individuals. Although the data in most cases cannot be released, due to privacy concerns, the models are usually made public or deployed as a service for inference on new test data. For a safe and secure use of machine learning models, it is important to have a quantitative assessment of the privacy risks of these models, and to make sure that they do not reveal sensitive information about their training data. This is of great importance as there has been a surge in the use of machine learning in sensitive domains such as medical and finance applications. Data Protection regulations, such as GDPR and AI governance frameworks, require personal data to be protected when used in AI systems, and that the users have control over their data and awareness about how it is being used. For example, Article 35 of GDPR requires organizations to systematically analyze, identify and minimize the data protecti","default_branch":null,"files":null,"tree":[],"storefront":"/r/privacytrustlab","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/privacytrustlab/ml_privacy_meter/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}