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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhao, Ben Y. Kruegel, Christopher Puttaswamy, Krishna P. N. |
| Abstract | By offering high availability and elastic access to resources, third-party cloud infrastructures such as Amazon EC2 are revolutionizing the way today's businesses operate. Unfortunately, taking advantage of their benefits requires businesses to accept a number of serious risks to data security. Factors such as software bugs, operator errors and external attacks can all compromise the confidentiality of sensitive application data on external clouds, by making them vulnerable to unauthorized access by malicious parties. In this paper, we study and seek to improve the confidentiality of application data stored on third-party computing clouds. We propose to identify and encrypt all functionally encryptable data, sensitive data that can be encrypted without limiting the functionality of the application on the cloud. Such data would be stored on the cloud only in an encrypted form, accessible only to users with the correct keys, thus protecting its confidentiality against unintentional errors and attacks alike. We describe Silverline, a set of tools that automatically 1) identify all functionally encryptable data in a cloud application, 2) assign encryption keys to specific data subsets to minimize key management complexity while ensuring robustness to key compromise, and 3) provide transparent data access at the user device while preventing key compromise even from malicious clouds. Through experiments with real applications, we find that many web applications are dominated by storage and data sharing components that do not require interpreting raw data. Thus, Silverline can protect the vast majority of data on these applications, simplify key management, and protect against key compromise. Together, our techniques provide a substantial first step towards simplifying the complex process of incorporating data confidentiality into these storage-intensive cloud applications. |
| Starting Page | 1 |
| Ending Page | 13 |
| Page Count | 13 |
| File Format | |
| ISBN | 9781450309769 |
| DOI | 10.1145/2038916.2038926 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-10-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Data confidentiality Cloud computing Program analysis |
| Content Type | Text |
| Resource Type | Article |
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