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首页> 外文会议>International Conference on Information Technology - New Generations >PPMark: An Architecture to Generate Privacy Labels Using TF-IDF Techniques and the Rabin Karp Algorithm
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PPMark: An Architecture to Generate Privacy Labels Using TF-IDF Techniques and the Rabin Karp Algorithm

机译：PPMark：使用TF-IDF技术和Rabin Karp算法生成隐私标签的架构

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Layman and non-layman users often have difficulties to understand privacy policy texts. The amount of time spent on reading and comprehending a policy poses a challenge to the user, who rarely pays attention to what he or she is agreeing to. Given this scenario, this paper aims to facilitate privacy policy terms presentation regarding data collection and sharing by introducing a new format called Privacy Label. Using natural language processing techniques, a model able to extract information about data collection in privacy policies and present them in an automated and easy-to-understand way to the user was built. To validate this model we used a precision assessment method where the accuracy of the extracted information was measured. The precision of our model was 0.685 (69%) when recovering information regarding data handling, making it possible for the final user to understand which data is being collected without reading the whole policy. The PPMark architecture can facilitate the notice-and-choice by presenting privacy policy information in an alternative way for online users.
机译：Layman和非外行用户经常难以理解隐私政策文本。在阅读和理解政策上花费的时间对用户来说挑战，谁很少关注他或她同意的东西。鉴于这种情况，本文旨在通过引入一个名为“隐私标签的新格式，促进关于数据收集和共享的隐私政策术语。使用自然语言处理技术，可以建立一个模型，该模型能够在隐私策略中提取有关数据收集的信息，并以自动且易于理解的方式呈现给用户。为了验证该模型，我们使用了一种精确评估方法，其中测量了提取信息的准确性。在恢复有关数据处理的信息时，我们模型的精度为0.685（69％），使最终用户可以理解在不阅读整个策略的情况下收集哪些数据。 PPMark架构可以通过以在线用户的替代方法呈现隐私政策信息来促进通知和选择。

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