Privacy-preserving labeling and classification of email

• Main Authors: POLURI RAVI K R, SEN MAINAK, RUDNICK CHRISTIAN, LUO YI, LI WEIGSHENG, ACHARYA SHARADA S
• Format: Patent
• Language: Chinese; English
• Published: 26.03.2021
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

Emails or other communications are labeled with a category label such as "spam" or "good" without using confidential or Personally Identifiable Information (PII). The category label is based on features of the emails such as metadata that do not contain PII. Graphs of inferred relationships between email features and category labels are used to assign labels to emails and to features of the emails. The labeled emails are used as a training dataset for training a machine learning model ("MLM"). The MLM model identifies unwanted emails such as spam, bulk email, phishing email, and emails that contain malware. 电子邮件或其他通信利用类别标记(诸如"垃圾"或"良好")而不使用机密或个人可标识信息(PII)而被标记。该类别标记基于该电子邮件的不包含PII的特征，诸如元数据。电子邮件特征与类别标记之间的推理关系的图被用于向电子邮件和电子邮件的特征指派标记。已标记电子邮件被用作用于训练机器学习模型("MLM")的训练数据集。MLM模型标识不想要的电子邮件，诸如垃圾、批量电子邮件、网络钓鱼电子邮件、和包含恶意软件的电子邮件。