Publication Type

Journal Article

Publication Date

5-2017

Abstract

Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires only lightweight computations like modular multiplications. We present a general OOPDP transformation framework which is applicable to PDP-related schemes with metadata aggregatability and public metadata expansibility. Following the framework, we present two efficient OOPDP instantiations.Technically, we present aggregatable vector Chemeleon hash functions which map a vector of values to a group element and play a central role in the OOPDP transformation. Theoretical and experimental analyses confirm that our technique is practical to speed-up PDP schemes.

Keywords

chameleon hash, cloud storage, data outsourcing, online/offline signature, Provable data possession

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Information Forensics and Security

Volume

12

Issue

5

First Page

1182

Last Page

1194

ISSN

1556-6013

Identifier

10.1109/TIFS.2017.2656461

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/TIFS.2017.2656461

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