Security Aware Partitioning for efficient file system search

Parker-Wood, Aleatha; Strong, Christina R.; Miller, Ethan L.; Long, Darrell D. E.

doi:10.1109/msst.2010.5496990

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is possible, and likely, that a real-world Percival installation would use a suitable replacement. For example, the secure partitioning scheme as defined by Parker-Wood et al [11] can be used as a secure and efficient way to store and quickly access the query server's shares.…”

Section: Query Servermentioning

confidence: 99%

Percival: A searchable secret-split datastore

Frank

Thurlow

Kroeger

et al. 2015

2015 31st Symposium on Mass Storage Systems and Technologies (MSST)

Self Cite

View full text Add to dashboard Cite

Abstract-Maintaining information privacy is challenging when sharing data across a distributed long-term datastore. In such applications, secret splitting the data across independent sites has been shown to be a superior alternative to fixed-key encryption; it improves reliability, reduces the risk of insider threat, and removes the issues surrounding key management. However, the inherent security of such a datastore normally precludes it from being directly searched without reassembling the data; this, however, is neither computationally feasible nor without risk since reassembly introduces a single point of compromise. As a result, the secret-split data must be pre-indexed in some way in order to facilitate searching. Previously, fixed-key encryption has also been used to securely pre-index the data, but in addition to key management issues, it is not well suited for long term applications.To meet these needs, we have developed Percival: a novel system that enables searching a secret-split datastore while maintaining information privacy. We leverage salted hashing, performed within hardware security modules, to access prerecorded queries that have been secret split and stored in a distributed environment; this keeps the bulk of the work on each client, and the data custodians blinded to both the contents of a query as well as its results. Furthermore, Percival does not rely on the datastore's exact implementation. The result is a flexible design that can be applied to both new and existing secret-split datastores. When testing Percival on a corpus of approximately one million files, it was found that the average search operation completed in less than one second.

show abstract

Section: Query Servermentioning

confidence: 99%

Percival: A searchable secret-split datastore

Frank

Thurlow

Kroeger

et al. 2015

2015 31st Symposium on Mass Storage Systems and Technologies (MSST)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recall, that in most high security systems, not only must the contents of unauthorized files be protected, so must the metadata and file name, making the file effectively invisible. Past work [6] addressed this issue by partitioning the files into clusters based on their security permissions. The effect would be that, for each partition, if a user could read one file in the partition, they could read every file in that partition.…”

Section: Indexingmentioning

confidence: 99%

Toward efficient search for ultrascale storage systems

Naps

Mokbel

2011

Proceedings of the First Annual Workshop on High Performance Computing Meets Databases

View full text Add to dashboard Cite

As the rate at which scientific computing generates data continues to increase, we are finding that managing this data, in all facets, is quickly becoming more challenging. In many facilities with large scale storage needs, this massive amount of data is stored in distributed, multi-tiered storage systems. It has become imperative to allow for efficient and effective search within these kinds of environments. For some search problems, specifically file system metadata, traditional relational databases have been used with, initially, good results. As the scale of supercomputing has grown though, we find that it is becoming increasing difficult for databases to scale up with the volume of metadata that they are managing. In this paper, we propose a new direction for database management techniques within the context of high performance computing, specifically, search within ultrascale storage systems. Instead of using databases as a layer sitting above the storage system, we suggest the movement of database components within the storage system itself. By taking this approach, we aim to leverage the decades of research and tuning that have made relational database technology successful. At the same time, this integration gives us the ability to maintain a better view of the storage system for search optimization. Through this effort, we can position these techniques to better scale to the degree that is required by the high performance computing community currently, and in the future.

show abstract

“…Currently, the state-of-the-art studies on the metadata search focus on using high-performance metadata index trees to accelerate metadata searching and enhance the accuracy ratio of queries, such as Ceph [6], Spyglass [1], SmartStore [7], Security Aware Partitioning [8], GIGA+ [9]. Popular multi-dimensional hierarchical indexing structures include the R-tree [10], the B-tree, the X-tree, the M-tree, the KD-tree [11] and their variants.…”

Section: Introductionmentioning

confidence: 99%