论文标题
#mumaSkup：在社交媒体平台上，对英语敏感发声的选择性属性加密
#maskUp: Selective Attribute Encryption for Sensitive Vocalization for English language on Social Media Platforms
论文作者
论文摘要
社交媒体已成为人们站起来并提高反对社会和犯罪行为的声音的平台。此类信息的发声允许调查和确定罪犯。但是，揭示这种敏感信息可能会危害受害者的安全。我们提出#maskup，这是一种安全地向相关当局提供安全通信的安全方法，阻止了受害者的潜在欺凌行为。这将通过补充有选择性加密的自然语言处理来保护其隐私，以保护其隐私。据我们所知，这是旨在通过掩盖其私人细节并鼓励他们挺身举报犯罪的第一项旨在保护受害者隐私的工作。掩蔽技术的使用仅允许绑定机构查看/掩盖此数据。我们在不断学习任务上构建和评估所提出的方法，从而在现实世界中实施了相同的方法。 #maskup成功地在验证提出的目标的示例数据集上成功演示了此集成。
Social media has become a platform for people to stand up and raise their voices against social and criminal acts. Vocalization of such information has allowed the investigation and identification of criminals. However, revealing such sensitive information may jeopardize the victim's safety. We propose #maskUp, a safe method for information communication in a secure fashion to the relevant authorities, discouraging potential bullying of the victim. This would ensure security by conserving their privacy through natural language processing supplemented with selective encryption for sensitive attribute masking. To our knowledge, this is the first work that aims to protect the privacy of the victims by masking their private details as well as emboldening them to come forward to report crimes. The use of masking technology allows only binding authorities to view/un-mask this data. We construct and evaluate the proposed methodology on continual learning tasks, allowing practical implementation of the same in a real-world scenario. #maskUp successfully demonstrates this integration on sample datasets validating the presented objective.
