OceanStore: an architecture for global-scale persistent storage
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
A Digital Signature Based on a Conventional Encryption Function
CRYPTO '87 A Conference on the Theory and Applications of Cryptographic Techniques on Advances in Cryptology
Farsite: federated, available, and reliable storage for an incompletely trusted environment
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Ceph: a scalable, high-performance distributed file system
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Cryptographic Security for a High-Performance Distributed File System
MSST '07 Proceedings of the 24th IEEE Conference on Mass Storage Systems and Technologies
Spark: cluster computing with working sets
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
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Large-scale storage systems often contain sensitive information such as medical information in healthcare services, captured traffic in large companies like Internet Service Providers, or personal user information which is stored by different firms. Most of these systems are distributed geographically and data should be able to move between systems in different places, which mean we cannot protect data by limiting them to internal access. Even internal access is not adequate to provide security because we cannot fully trust every operator who has physical access to systems. In this paper we propose a new approach which provides both confidentiality and integrity for large datasets by combining keyed hash tree (KHT) and Merkle hash tree. In addition, we introduce a hash value and key exchange protocol. Theoretical and experimental analysis shows that it is a practical and scalable way to protect large-scale datasets against modification and theft.