Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage
Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage
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发表时间：
2021
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通讯作者：
Mohammad Taha Khan;Christopher Tran;Shubham Singh;Dimitri Vasilkov;Chris Kanich;Blase Ur;E. Zheleva
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作者：
Mohammad Taha Khan;Christopher Tran;Shubham Singh;Dimitri Vasilkov;Chris Kanich;Blase Ur;E. Zheleva
随着数据泄露的普遍存在，存储在云中的被遗忘的文件会产生潜在的隐私风险。我们采取全面的方法帮助用户识别云存储中的敏感、不需要的文件。我们首先进行了17次定性访谈，以描述使人类认为文件敏感，有用，值得保护或删除的因素。基于我们的发现，我们进行了一项主要的定量在线研究。我们向108名Google Drive或Dropbox的长期用户展示了他们帐户中的精选文件。他们标记并解释这些文件的敏感性，有用性和所需的管理（无论他们想保留，删除还是保护它们）。对于每个文件，我们收集了许多元数据和内容特征，构建了3，525个标记文件的训练数据集。然后，我们建立了Aletheia，它可以预测一个文件的感知敏感性和有用性，以及它所期望的管理。Aletheia在最先进的基线上提高了26%到159%，预测用户所需的文件管理决策的准确率为79%。值得注意的是，预测有用性和敏感性的主观感知导致预测所需文件管理决策的绝对准确性提高了10%。Aletheia的性能验证了在主观安全相关任务上使用推理技术时以人为中心的特征选择方法。它还改进了最小化云帐户攻击面的最新技术。
With the ubiquity of data breaches, forgotten-about files stored in the cloud create latent privacy risks. We take a holistic approach to help users identify sensitive, unwanted files in cloud storage. We first conducted 17 qualitative interviews to characterize factors that make humans perceive a file as sensitive, useful, and worthy of either protection or deletion. Building on our findings, we conducted a primarily quantitative online study. We showed 108 long-term users of Google Drive or Dropbox a selection of files from their accounts. They labeled and explained these files’ sensitivity, usefulness, and desired management (whether they wanted to keep, delete, or protect them). For each file, we collected many metadata and content features, building a training dataset of 3,525 labeled files. We then built Aletheia, which predicts a file’s perceived sensitivity and usefulness, as well as its desired management. Aletheia improves over state-of-the-art baselines by 26% to 159%, predicting users’ desired file-management decisions with 79% accuracy. Notably, predicting subjective perceptions of usefulness and sensitivity led to a 10% absolute accuracy improvement in predicting desired file-management decisions. Aletheia’s performance validates a human-centric approach to feature selection when using inference techniques on subjective security-related tasks. It also improves upon the state of the art in minimizing the attack surface of cloud accounts.