Percival: A searchable secret-split datastore

Frank, Joel C.; Thurlow, Lincoln; Kroeger, Thomas M; Miller, Ethan L.; Long, Darrell D. E.

doi:10.1109/msst.2015.7208296

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Losing the index to an attacker does not compromise data of other users. Percival [2], a query-based system provides us with a subset of relevant shares within a secret-split datastore. While these systems differ in the way that they split data across multiple archives, our techniques for rebuilding data can work with any of them, as well as other systems that distribute data across multiple systems such as SafeStore [19].…”

Section: Systems Using Information Dispersalmentioning

confidence: 99%

“…Under this approach, data is split into n chunks so that at least m ≤ n of them are required to rebuild the original data, often using techniques based on Shamir's secret-splitting [1]. The chunks are then distributed across n administratively-isolated servers, ensuring both the reliability and availability of the data while also protecting it from internal attacks [2], [3].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Reconstruction Techniques for Disaster Recovery in Secret-Split Datastores

Mukhopadhyay

Frank

King

et al. 2018

2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

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Increasingly, archival systems are relying on authentication-based techniques that leverage secret-splitting rather than encryption to secure data for long-term storage. Secret-splitting data across multiple independent repositories reduces complexities in key management, eliminates the need for updates due to encryption algorithm deprecation over time, and reduces the risk of insider compromise. While reconstruction of stored data objects is straightforward if a user-maintained index is available, the system must also support disaster recovery incase the index is unavailable. Designing a mechanism for efficient index-free reconstruction, that does not increase the risk of attacker compromise, is a challenge. Reconstruction requires the association of chunks that make up an object, which is the kind of information attackers can use to identify chunks they must steal to illicitly obtain data.We propose two new techniques, the set-subset reconstruction and secret-split secure hash (S3H) reconstruction, which allow chunks of data to be correlated and quickly reconstructed without providing useful information to an attacker. Both techniques operate on the entire collections of secret-split chunks in the archive. While they can efficiently rebuild an entire archive, they are inefficient and impractical for rebuilding single objects, making them useless for attackers that do not have access to all of the data. These techniques can each be tuned to trade-off between reconstruction performance and security, reducing overall runtime from O(N K ) (for N objects requiring K recombined chunks each to return the original object) to between O(N ) and O(N 2 ). These runtimes are practical for archives containing as many as 10 7 objects for the secret-split secure hash method and 10 9 objects for the set-subset method. Larger archives can run these techniques with manageable runtimes by grouping data into separate smaller collections and running the algorithms on each collection in parallel.

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Section: Systems Using Information Dispersalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Reconstruction Techniques for Disaster Recovery in Secret-Split Datastores

Mukhopadhyay

Frank

King

et al. 2018

2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

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“…Percival [89] and Potshards [250] are two systems relying on secret-splitting techniques. The latter offers long-term security by using two levels of secret splitting and placement.…”

Section: Censorship Resistancementioning

confidence: 99%

Composing private and censorship-resistant solutions for distributed storage

Burihabwa¹

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Cloud storage has durably entered the stage as go-to solution for business and personal storage. Virtually extending storage capabilities to infinity, cloud storage enables companies and individuals to focus on content creation without fear of running out of space or losing data. But as users entrust more and more data to the cloud, they also have to accept a loss of control over the data they o˜oad to the cloud. At a time when online services seem to be making a significant part of their profits by exploiting customer data, concerns over privacy and integrity of said data naturally arise. Are their online documents read by the storage provider or its employees? Is the content of these documents shared with third party partners of the storage provider? What happens if the provider goes bankrupt? Whatever answer can be o˙ered by the storage provider, the loss of control should be cause for concern. But storage providers also have to worry about trust and reliability. As they build distributed solutions to accommodate their customers’ needs, these concerns of control extend to the infrastructure they operate on. Conciliating security, confidentiality, resilience and perform-ance over large sets of distributed storage nodes is a tricky balancing act. And even when a suitable balance can be found, it is often done at the expense of increased storage overhead. In this dissertation, we try to mitigate these issues by focusing on three aspects. First, we study solutions to empower users with flexible tooling ensuring security, integrity and redundancy in distributed storage settings. By leveraging public cloud storage o˙erings to build a configurable file system and storage middleware, we show that securing cloud-storage from the client-side is an e˙ective way maintaining control. Second, we build a distributed archive whose resilience goes beyond standard redundancy schemes. To achieve this, we implement Recast, relying on a data entanglement scheme, that encodes and distributes data over a set of storage nodes to ensure durability at a manageable cost. Finally, we look into o˙setting the increase in storage overhead by means of data reduction. This is made possible by the use of Generalised Deduplication, a scheme that improves over classical data deduplication by detecting similarities beyond exact matches.

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