Towards Measuring Risk Factors in Privacy Policies
Towards Measuring Risk Factors in Privacy Policies
复制标题
衡量隐私政策中的风险因素
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
D. Collarana
中科院分区：
文献类型：
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作者：
Najmeh Mousavi Nejad;D. Graux;D. Collarana
在线服务和移动应用程序的无处不在导致隐私政策形式的合同协议迅速扩散。尽管此类同意书很重要，但由于其内容长度和复杂性，大多数用户往往会忽略它们。因此，用户可能同意与欧盟法律中的 GDPR 等法律法规不符的政策。在本研究中，我们提出了一种混合方法，该方法应用监督深度学习和基于规则的信息提取来衡量隐私政策的风险因素。受益于包含 115 项隐私政策的带注释数据集，深度学习组件首先能够预测每个段落的高级类别。然后，基于规则的模块根据高级类提取预定义的属性及其值。最后，根据这些属性值计算隐私策略的风险因子。
The ubiquitous availability of online services and mobile apps results in a rapid proliferation of contractual agreements in the form of privacy policies. Despite the importance of such consent forms, the majority of users tend to ignore them due to their content length and complexity. Thus, users might be consenting policies that are not aligned to regulations in laws such as the GDPR from the EU law. In this study, we propose a hybrid approach which measures a privacy policy’s risk factor applying both supervised deep learning and rule-based information extraction. Benefiting from an annotated dataset of 115 privacy policies, a deep learning component is first able to predict high-level categories for each paragraph. Then, a rule-based module extracts pre-defined attributes and their values, based on high-level classes. Finally, a privacy policy’s risk factor is computed based on these attribute values.