Towards securing cloud data in the multi-cloud scenario

Cloud computing has emerged to be the accepted computing model which provides services on-demand. The most used service layer is infrastructure as a Service (IaaS) to outsource data to the cloud. With this service, organizations and individuals can avail of cloud services in pay as you use fashion instead of investing money for such infrastructure. Cloud provides many such benefits to its users. However, as the cloud servers are remote and assumed to be untrusted, users are worried about data security. Initially, a single cloud was used to store data. With the advancements in technologies and for reliability reasons, the concept of multi-cloud has emerged. The security and reliability issues with a single cloud can be overcome with multi-cloud systems. The rationale behind this is that a single cloud might have malicious insiders. When two or more clouds collaborate and provide services to end-users, it is expected to have more reliability and possible reduction in malicious insiders. This paper focuses on studying the potential security of data that is stored in multi-cloud. We built an algorithm and prototype application that demonstrates the concept of securing data in a multi-cloud environment. The empirical results revealed that the proposed system could ensure the data outsourced to cloud computing where a multi-cloud scenario prevails

Fast Lean Erasure-Coded Atomic Memory Object

In this work, we propose FLECKS, an algorithm which implements atomic memory objects in a multi-writer multi-reader (MWMR) setting in asynchronous networks and server failures. FLECKS substantially reduces storage and communication costs over its replication-based counterparts by employing erasure-codes. FLECKS outperforms the previously proposed algorithms in terms of the metrics that to deliver good performance such as storage cost per object, communication cost a high fault-tolerance of clients and servers, guaranteed liveness of operation, and a given number of communication rounds per operation, etc. We provide proofs for liveness and atomicity properties of FLECKS and derive worst-case latency bounds for the operations. We implemented and deployed FLECKS in cloud-based clusters and demonstrate that FLECKS has substantially lower storage and bandwidth costs, and significantly lower latency of operations than the replication-based mechanisms

Succinct Erasure Coding Proof Systems

Erasure coding is a key tool to reduce the space and communication overhead in fault-tolerant distributed computing. State-of-the-art distributed primitives, such as asynchronous verifiable information dispersal (AVID), reliable broadcast (RBC), multi-valued Byzantine agreement (MVBA), and atomic broadcast, all use erasure coding. This paper introduces an erasure coding proof (ECP) system, which allows the encoder to prove succinctly and non-interactively that an erasure-coded fragment is consistent with a constant-sized commitment to the original data block. Each fragment can be verified independently of the other fragments. Our proof system is based on polynomial commitments, with new batching techniques that may be of independent interest. To illustrate the benefits of our ECP system, we show how to build the first AVID protocol with optimal message complexity, word complexity, and communication complexity

Space Bounds for Reliable Storage:Fundamental Limits of Coding

We study the inherent space requirements of shared storage algorithms in asynchronous fault-prone systems. Previous works use codes to achieve a better storage cost than the well-known replication approach. However, a closer look reveals that they incur extra costs somewhere else: Some use unbounded storage in communication links, while others assume bounded concurrency or synchronous periods. We prove here that this is inherent, and indeed, if there is no bound on the concurrency level, then the storage cost of any reliable storage algorithm is at least f+1 times the data size, where f is the number of tolerated failures. We further present a technique for combining erasure-codes with full replication so as to obtain the best of both. We present a storage algorithm whose storage cost is close to the lower bound in the worst case, and adapts to the concurrency level