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CloudDLP: Transparent and scalable data sanitization for browser-based cloud storage

  • Peiyi Han
  • , Chuanyi Liu*
  • , Jiahao Cao
  • , Shaoming Duan
  • , Hezhong Pan
  • , Zekun Cao
  • , Binxing Fang
  • *Corresponding author for this work
  • Beijing University of Posts and Telecommunications
  • Harbin Institute of Technology
  • Peng Cheng Laboratory
  • Tsinghua University
  • CAS - Institute of Computing Technology
  • University of Chinese Academy of Sciences

Research output: Contribution to journalArticlepeer-review

Abstract

Browser-based cloud storage services are still broadly used in enterprises for online sharing and collaboration. However, sensitive information in images or documents may be easily leaked outside trusted enterprise on-premises due to such cloud services. Existing solutions to prevent data leakage in cloud storage services either limit many functionalities of cloud applications or are difficult to be scaled to various cloud applications. In this paper, we propose CloudDLP, a transparent and scalable approach for enterprises to automatically sanitize sensitive data in images and documents with various browser-based cloud applications. CloudDLP is deployed as an internet gateway within the premises of an enterprise using JavaScript injecting techniques and deep learning methods to sanitize sensitive premise data. It neither compromises the user experience nor significantly affects application functionalities in browser-based cloud storage services. We have evaluated CloudDLP with a number of real-world cloud applications. Our experimental results show that it can achieve automatic data sanitization with cloud storage services while preserving most functionalities of cloud applications.

Original languageEnglish
Article number9057526
Pages (from-to)68449-68459
Number of pages11
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Data loss prevention
  • cloud storage
  • data sanitization
  • data security

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