跨域非结构化文档的实用性去识别:一种具有关系提取过的实用性保护方法
Liubov Nedoshivina1, Anisa Halimi1, Joao Bettencourt-Silva1
1IBM Research Europe Dublin, Ireland.
概括
我们开发了一种新的方法来消除敏感文件的识别,改善数据隐私. 这种方法减少了错误并保持了信息的实用性,有助于遵守隐私法规.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 数据 隐私 数据 隐私 数据
背景情况:
- 每天生成大量的个人信息需要强有力的隐私措施.
- 遵守不断变化的全球隐私法规对于利用数据至关重要.
- 现有的非识别方法在平衡隐私与数据实用性方面扎.
研究的目的:
- 引入READI,这是一个用于去识别非结构化文档的新框架.
- 通过改进实体检测来提高数据非识别质量.
- 评估READI在减少虚假阳性和保护数据实用性的有效性.
主要方法:
- 使用命名实体识别 (NER) 和关系提取 (RE) 技术.
- 开发一个名为READI的公用事业保护框架.
- 对两种不同的数据集的方法与最先进的方法进行评估.
主要成果:
- READI显著降低了被删除标识文本中的错误阳性.
- 基于关系提取的去识别方法 (READI) 提高了去识别数据的实用性.
- 与现有技术相比,在多个数据集上证明了有效性.
结论:
- READI为非结构化文档去识别提供了一种优越的方法.
- 该框架有效地平衡了隐私保护和数据可用性.
- READI帮助组织满足个人信息的合规要求.
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