Visible to the public CloudDLP: Transparent and Automatic Data Sanitization for Browser-Based Cloud Storage

TitleCloudDLP: Transparent and Automatic Data Sanitization for Browser-Based Cloud Storage
Publication TypeConference Paper
Year of Publication2019
AuthorsLiu, Chuanyi, Han, Peiyi, Dong, Yingfei, Pan, Hezhong, Duan, Shaoming, Fang, Binxing
Conference Name2019 28th International Conference on Computer Communication and Networks (ICCCN)
Date PublishedAug. 2019
PublisherIEEE
ISBN Number978-1-7281-1856-7
Keywordsautomatic data sanitization, browser-based cloud storage, Browsers, cloud computing, cloud services, CloudDLP, compositionality, data deletion, data privacy, Data Sanitization, Deep Learning, document image processing, Encryption, Human Behavior, human factors, learning (artificial intelligence), privacy, Protocols, pubcrawl, resilience, Resiliency, Scalability, sensitive data, sensitive image documents, sensitive information, textual documents, transparent data sanitization, user data privacy
Abstract

Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities.

URLhttps://ieeexplore.ieee.org/document/8846948
DOI10.1109/ICCCN.2019.8846948
Citation Keyliu_clouddlp_2019