Data Corruption

About: Data Corruption is a research topic. Over the lifetime, 435 publications have been published within this topic receiving 6784 citations.

Overcoming data corruption in RS485 communication

Abstract: Data corruption is a frequent problem encountered in RS-485 communication systems. Most of the time this happens due to transmission line effects, imbalance in impedance in balanced pair, inadequate shielding and grounding of cables.

GFCache: a Greedy Failure Cache Considering Failure Recency and Failure Frequency for an Erasure-coded Storage System

Abstract: In the big data era, data unavailability, either temporary or permanent, becomes a normal occurrence on a daily basis. Unlike the permanent data failure, which is fixed through a background job, temporarily unavailable data is recovered on-the-fly to serve the ongoing read request. However, those newly revived data is discarded after serving the request, due to the assumption that data experiencing temporary failures could come back alive later. Such disposal of failure data prevents the sharing of failure information among clients, and leads to many unnecessary data recovery processes, (e.g. caused by either recurring unavailability of a data or multiple data failures in one stripe), thereby straining system performance. To this end, this paper proposes GFCache to cache corrupted data for the dual purposes of failure information sharing and eliminating unnecessary data recovery processes. GFCache employs a greedy caching approach of opportunism to promote not only the failed data, but also sequential failure-likely data in the same stripe. Additionally, GFCache includes a FARC (Failure ARC) catch replacement algorithm, which features a balanced consideration of failure recency, frequency to accommodate data corruption with good hit ratio. The stored data in GFCache is able to support fast read of the normal data access. Furthermore, since GFCache is a generic failure cache, it can be used anywhere erasure coding is deployed with any specific coding schemes and parameters. Evaluations show that GFCache achieves good hit ratio with our sophisticated caching algorithm and manages to significantly boost system performance by reducing unnecessary data recoveries with vulnerable data in the cache.

Publicly-Verifiable Proofs of Storage Based on the Discrete Logarithm Problem

Abstract: With the rapid development of cloud computing platforms, cloud storage services are becoming widespread in recent years. Based on these services, clients are able to store data on remote cloud servers and thereby saving their local storage. This greatly reduces the burden of clients, while it also brings certain security risks to the outsourced data. Among the risks, a critical one is data corruption, for example cloud servers may delete some rarely used outsourced data for cost saving. To prevent this risk, proof of storage (PoS) schemes are invented, which can validate the integrity of cloud data without downloading the entire data. The existing PoS schemes, however, mostly either involve complex operations e.g. bilinear pairings, or don't support public verifiability. To fill this gap, in this paper we construct a new PoS scheme that is publicly verifiable and only requires simple cryptographic computations. We prove that our scheme is secure under the discrete logarithm assumption, in the random oracle model. Furthermore, we also show how to extend the scheme to support data updates. Finally, we implement our scheme. The simulation results demonstrate that our scheme is more computationally-efficient than the publicly-verifiable PoS schemes of Shacham and Waters (Journal of Cryptology 2013).

Surviving a computer disaster

Abstract: One used to think of a business disaster as the inferno, tempest or flood that destroyed buildings, equipment and records vital to the running of a company. Of course, such disasters still happen, but an increasing number-indeed the majority-of disasters are less physical and more subtle. They include: system failure and data corruption caused by electromagnetic interference; loss of processing capability through police denying access to computer installations as a result of nearby terrorist activity; chemical contamination of hardware and magnetic media; illegal access (hacking) into computer systems resulting in corruption or loss of data; theft of personal computers; rodents; loss of supplied services; and loss of power. >

Data preprocessing using hybrid general regression neural networks and particle swarm optimization for remote terminal units

Abstract: Data corruption in SCADA systems refers to errors that occur during acquisition, processing, or transmission, introducing unintended changes to the original data. In SCADA-based power systems, the data gathered by remote terminal units (RTUs) is subject to data corruption due to noise interference or lack of calibration. In this study, an effective approach based on the fusion of the general regression neural network (GRNN) and the particle swarm optimization (PSO) technique is employed to deal with errors in RTU data. The proposed hybrid model, denoted as GRNN-PSO, is able to handle noisy data in a fast speed, which makes it feasible for practical applications. Experimental results show the GRNN-PSO model has better performance in removing the unintended changes to the original data compared with existing methods.