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Efficient Verifiable Unbounded-Size Database From Authenticated Matrix Commitment
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AI
DOI：10.1109/TDSC.2022.3225283.png)
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Abstract
En 中文
Verifiable database with update (VDB) enables the client to store a large dataset in the outsourced database, and then efficiently query and update the data with a new value. It is attractive for the merits of checking the validity of the queried data and detecting the malicious actions of tampering with the outsourced database concurrently. However, the database in the context of VDB is merely suitable to store a fixed-size dataset. Hence, VDB is inapplicable to the unbounded-size database that provides the capability to store and manage the arbitrary-size datasets in the incremental manners. To circumvent the weaknesses, we research on the verifiable unbounded-size database with update (VUSDB). The VUSDB is sufficient for multiple clients to store their own arbitrary-size datasets in the database that has already contained some datasets. In order to design a VUSDB scheme, we first put forward a primitive called authenticated matrix commitment and give a scheme. This primitive is qualified to commit to a collection of ordered data represented in the form of matrix, and assure the ownership of the opened data. Then we utilize the authenticated matrix commitment scheme to construct a VUSDB scheme. The performance evaluation shows that the proposed schemes are efficient and practical.
Keywords:
Databases
Cloud computing
Servers
Indexes
Authentication
Security
Costs
Cloud computing
outsourced database
verifiable database
verifiable computation
Journal
IF:
7.5
Papers:
2.4K
Citations:
9.6K