Study on the technical evaluation of decentralization based de-identification procedures for personal data in the automotive sector

The General Data Protection Regulation (GDPR) has significantly increased the incentive and effort for companies to process personal data in compliance with the law. This includes the creation, distribution, storage and deletion of personal data. Non-compliance with the GDPR and other legislation now poses a significant financial risk to companies that work with personal data. Because of this reason, the present project studied on the technical evaluation of decentralization based on de-identification procedures for personal data in the automotive sector.
For this, use cases were identified through a scientific literature review. The following use cases were identified and analyzed with regard to data, benefits, model and sensible data: Traffic flow prediction, Energy demand prediction, Eco-routing, Autonomous driving, Vehicular object detection, Parking space estimation.
Furthermore, attack scenarios and general countermeasures against these attacks were discussed. To do so, relevant transmission paths, data types and trust scenarios were considered.

《通用数据保护条例》（GDPR）显著提升了企业按照法律规定处理个人数据的动力与投入，这涵盖了个人数据的创建、分发、存储及删除等环节。违反GDPR及其他相关法规，现已成为涉及个人数据工作的企业面临的一大财务风险。鉴于这一原因，本项研究对基于脱敏程序的汽车行业个人数据去中心化技术评估进行了探讨。为此，通过文献综述确定了相关用例。以下用例被识别并进行了分析，涵盖了数据、效益、模型及敏感数据等方面：交通流量预测、能源需求预测、环保路线规划、自动驾驶、车辆目标检测、停车位估算。此外，还讨论了针对这些攻击的攻击场景和一般性对策。为此，考虑了相关的传输路径、数据类型及可信场景。