Preventing Pollution Attacks in Cloud Storages

Abstract

Cloud storage is a cloud-computing model in which data is stored on remote servers accessed from the internet. It has significantly changed the way users and administrators manage and access their data. Using remote storages to store data has many advantages in terms of availability and operational costs, but the security of such data is still one of the major concerns for the users. Pollution attack, where an adversary modifies some of the stored data is one of the many potent risks that affect the cloud data. In this paper, we show how disastrous pollution attack can be in coding based block level cloud storages, and how our algorithm using LRC, a version of Raptor codes, can identify an attack even before decoding all of the received packets.