While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
NL2GDPR: Automatically Develop GDPR Compliant Android Application Features from Natural Language
Baidu Research（百度研究院）
最近的隐私泄露事件和更严格的政策法规要求公司和移动应用程序的合规标准要高得多。然而，这些义务也给应用程序开发人员带来了重大挑战，他们需要遵守这些包含各种观点、活动和角色的法规，特别是对于在这方面经验较少或资源有限的小公司和开发人员。为了解决这些障碍，我们开发了一个自动工具NL2GDPR，它可以从开发人员的自然语言描述中生成策略，同时还确保应用程序的功能符合通用数据保护条例（GDPR）。NL2 GDPR是利用百度认知计算实验室开发的信息抽取工具OIA（Open Information Annotation）开发的。NL2GDPR的核心是一个以隐私为中心的信息抽取模型，并附加了GDPR策略查找器和策略生成器。我们进行了全面的研究，以掌握在提取以隐私为中心的信息和生成隐私政策的挑战，同时利用优化这一特定的任务。借助NL2GDPR，我们可以分别实现92.9%、95.2%和98.4%的准确率，正确识别与个人数据存储、处理和共享类型相关的GDPR政策。据我们所知，NL2GDPR是第一个允许开发人员自动生成GDPR合规策略的工具，只需要输入自然语言来描述应用程序功能。请注意，其他非GDPR相关功能可能与生成的功能集成，以构建复杂的应用程序。
Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
School of Computing, Mathematics and Engineering, Charles Sturt University（查尔斯斯特大学计算、数学与工程学院）；AI and Cyber Futures Centre, Charles Sturt University（查尔斯斯特大学人工智能与网络未来中心）
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
Department of CST Tsinghua University Beijing, China（计算机科学与技术系 清华大学 北京中国）；Department of CSE Chandigarh University Mohali, India（计算机科学与工程系 印度昌迪加尔大学 摩哈利）
