Information Flow Auditing in the Cloud release_krqhhu3kl5gsjncedr3kjd5oe4

by Angeliki Zavou

Published by Columbia University.

2017  

Abstract

As cloud technology matures and trendsetters like Google, Amazon, Microsoft, Apple, and VMware have become the top-tier cloud services players, public cloud services have turned mainstream for individual users. In this work, I propose a set of techniques that can be used as the basis for alleviating cloud customers' privacy concerns and elevating their condence in using the cloud for security-sensitive operations as well as trusting it with their sensitive data. The main goal is to provide cloud customers' with a reliable mechanism that will cover the entire path of tracking their sensitive data, while they are collected and used by cloud-hosted services, to the presentation of the tracking results to the respective data owners. In particular, my design accomplishes this goal by retrofitting legacy applications with data flow tracking techniques and providing the cloud customers with comprehensive information flow auditing capabilities. For this purpose, we created CloudFence, a cloud-wide fine-grained data flow tracking (DFT) framework, that sensitive data in well-defined domains, offering additional protection against inadvertent leaks and unauthorized access.
In text/plain format

Archived Files and Locations

application/pdf  1.7 MB
file_zrdzimpf4bcipptam5czlemwcq
www.cs.columbia.edu (web)
web.archive.org (webarchive)
application/pdf  1.7 MB
file_72powj4etnccfhvi7o5wmnt57m
academiccommons.columbia.edu (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2017-06-12
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 3a89b61b-3bc9-45f9-8167-34cdb7547091
API URL: JSON