{"repo":"deepanwadhwa/zink","free":true,"listed":false,"github":"https://github.com/deepanwadhwa/zink","clone":"git clone https://github.com/deepanwadhwa/zink.git","description":"A Python package for zero-shot text anonymization using Transformer-based NER models.","language":"Python","stars":84,"topics":["ai","anonymization","hippaa","machine-learning","nlp","privacy"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"ZINK (Zero-shot Ink) ZINK is a Python package designed for zero-shot anonymization of entities within unstructured text data. It allows you to redact or replace sensitive information based on specified entity labels. Abstract The proliferation of Large Language Models (LLMs) heightens challenges in protecting Personal Identifiable Information (PII), particularly Quasi-Identifiers (QIs), in unstructured text. QIs enable re-identification when combined and pose significant privacy risks, highlighted by their use in security verification. Current approaches face limitations: large LLMs offer flexibility for detecting diverse QIs but are often hindered by high computational costs, while traditional supervised NER models require domain-specific labeled data and fail to generalize to heterogeneous, unseen QI types. Furthermore, evaluating QI identification methods is hampered by the lack of diverse benchmarks. To address this need for evaluation resources, we present the Quasi-Identifier Benchmark (QIB), a new corpus with 1750 examples across 35 diverse QI categories (e.g., personal preferences, security answers) designed to assess model robustness against QI heterogeneity. To facilitate the application of flexible identification methods on such diverse data, we also introduce ZINK (Zero-shot INK), a Python package providing a unified framework for applying existing zero-shot NER models to QI identification and anonymization, simplifying model integration and offering configurable ","default_branch":null,"files":null,"tree":[],"storefront":"/r/deepanwadhwa","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/deepanwadhwa/zink/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."}