Título :	Hy-SAIL : hyper-scalability, availability and integrity layer for cloud storage systems
Autor :	Amaral, Dino Macedo Gondim, João José Costa Albuquerque, Robson de Oliveira Sandoval Orozco, Ana Lucila García Villalba, Luis Javier
Assunto::	Computação em nuvem Armazenamento de dados Teoria da codificação
Fecha de publicación :	5-jul-2019
Editorial :	IEEE
Citación :	AMARAL, Dino Macedo et al. Hy-SAIL : hyper-scalability, availability and integrity layer for cloud storage systems. IEEE Access, v. 7, 2019. DOI: 10.1109/ACCESS.2019.2925735. Disponível em: https://ieeexplore.ieee.org/abstract/document/8756118. Acesso em: 05 mar. 2020.
Abstract:	Cloud storage has gained increasing attention from the industry and research communities with concerns about data stored in cloud computing environments, despite its many advantages. The reasons come from economic viability to the latency along with behavioral changes that may affect the applications that use data stored remotely. Among those challenges, there is the problem of ensuring the integrity and retrievability of users' data in the cloud. Several schemas have been proposed: proof of data possession (PDP), proof of retrievability (PoR), and proof of ownership (PoW) that differ on the approach to guarantee the effective retrieval of data stored remotely. In this paper, a novel PoR protocol is proposed: hyperscalability, availability, and integrity layer (Hy-SAIL), where a new PoR scheme adds higher availability to data stored and a exible manner to perform integrity checks. It is demonstrated that Hy-SAIL leads to an ef cient and scalable cryptographic system that meets near-optimal bounds in terms of communication and storage complexities. It is also proposed a new adversarial model that aggregates the main functionalities of a realistic adversary in cloud computing environments. Hy-SAIL operates on data that are not affected by any kind of incremental change or update, which is the characteristic of various le types stored in clouds, such as stored video and audio streams. When compared to other systems, such as HAIL, Hy-SAIL is more scalable and ef cient. The results collected with an unoptimized implementation of Hy-SAIL point to a better perspective than other approaches.
Licença::	This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
DOI:	10.1109/ACCESS.2019.2925735
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