Online/Offline Provable Data Possession
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.