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—Searchable encryption has received a significant attention from the research community with various constructions being proposed, each achieving asymptotically optimal complexity for specific metrics (e.g., search, update). Despite their elegance, the recent attacks and deployment efforts have shown that the optimal asymptotic complexity might not always imply practical performance, especially if the application demands high privacy. In this article, we introduce a novel Dynamic Searchable Symmetric Encryption (DSSE) framework called Incidence Matrix (IM)-DSSE, which achieves a high level of privacy, efficient search/update, and low client storage with actual deployments on real cloud settings. We harness an incidence matrix along with two hash tables to create an encrypted index, on which both search and update operations can be performed effectively with minimal information leakage. This simple set of data structures surprisingly offers a high level of DSSE security while achieving practical performance. Specifically, IM-DSSE achieves forward-privacy, backward-privacy, and size-obliviousness simultaneously. We also create several DSSE variants, each offering different trade-offs that are suitable for different cloud applications and infrastructures. We fully implemented our framework and evaluated its performance on a real cloud system (Amazon EC2). We have released IM-DSSE as an open-source library for wide development and adaptation.
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Efficient Dynamic Proof of Retrievability for Cold Storage
Storage-as-a-service (STaaS) permits the client to outsource her data to the cloud, thereby reducing data management and maintenance costs. However, STaaS also brings significant data integrity and soundness concerns since the storage provider might not keep the client data intact and retrievable all the time (e.g., cost saving via deletions). Proof of Retrievability (PoR) can validate the integrity and retrievability of remote data effectively. This technique can be useful for regular audits to monitor data compromises, as well as to comply with standard data regulations. In particular, cold storage applications (e.g., MS Azure, Amazon Glacier) require regular and frequent audits with less frequent data modification. Yet, despite their merits, existing PoR techniques generally focus on other metrics (e.g., low storage, fast update, metadata privacy) but not audit efficiency (e.g., low audit time, small proof size). Hence, there is a need to develop new PoR techniques that achieve efficient data audit while preserving update and retrieval performance. In this paper, we propose Porla, a new PoR framework that permits efficient data audit, update, and retrieval functionalities simultaneously. Porla permits data audit in both private and public settings, each of which features asymptotically (and concretely) smaller audit-proof size and lower audit time than all the prior works while retaining the same asymptotic data update overhead. Porla achieves all these properties by composing erasure codes with verifiable computation techniques which, to our knowledge, is a new approach to PoR design. We address several challenges that arise in such a composition by creating a new homomorphic authenticated commitment scheme, which can be of independent interest. We fully implemented Porla and evaluated its performance on commodity cloud (i.e., Amazon EC2) under various settings. Experimental results demonstrated that Porla achieves two to four orders of magnitude smaller audit proof size with 4x–18000x lower audit time than all prior schemes in both private and public audit settings at the cost of only 2x–3x slower update.
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- Award ID(s):
- 1917627
- PAR ID:
- 10486511
- Publisher / Repository:
- Internet Society
- Date Published:
- ISBN:
- 1-891562-83-5
- Subject(s) / Keyword(s):
- Authentication Cloud security Proof of Retrievability Cold storage
- Format(s):
- Medium: X
- Location:
- San Diego, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.14722/ndss.2023.23307
