Data Corruption

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

A Survey on Audit Free Cloud Storage via Deniable Attribute Based Encryption

Abstract: Cloud computing is a rising technology which provider an assortment of opportunities for online distribution of resources or services. The most effective advantage of using cloud computing is higher availability of services with less cost and simple scalability. While the storage space of shared data on remote servers is not a new development, current development of cloud computing validates a more careful look at its actual consequences involving privacy and confidentiality issues. As users no longer actually possess the storage of their data, traditional cryptographic primitives for the purpose of data protection cannot be directly accepted. In particular, simply downloading all the data for its integrity confirmation is not a realistic explanation due to the expensiveness in I/O and transmission cost across the network. Besides, it is often not enough to detect the data corruption only when contacting the data, as it does not offer users correctness assurance for those un-accessed information and might be too late to recover the data loss or damage. To fully make sure the data integrity and save the cloud users’ calculation resources as well as online burden, it is of critical importance to allow public auditing service for cloud data, so that users may choice to an independent third party auditor (TPA) to audit the contract out data when needed. The TPA, who has expertise and abilities that users do not, can occasionally check the honesty of all the data stored in the cloud on behalf of the users, which provides a much more better and reasonable way for the users to ensure their storage rightness in the cloud. In a word, allowing public auditing services will play an important role for this emerging cloud market to become fully recognized; where users will need ways to evaluate risk and gain hope in the cloud.

Finding Error-Handling Bugs in Systems Code Using Static Analysis

Abstract: Run-time errors are unavoidable whenever software interacts with the physical world. Unchecked errors are especially pernicious in operating system file management code. Transient or permanent hardware failures are inevitable, and errormanagement bugs at the file system layer can cause silent, unrecoverable data corruption. Furthermore, even when developers have the best of intentions, inaccurate documentation can mislead programmers and cause software to fail in unexpected ways. We use static program analysis to understand and make error handling in large systems more reliable. We apply our analyses to numerous Linux file systems and drivers, finding hundreds of confirmed error-handling bugs that could lead to serious problems such as system crashes, silent data loss and corruption.

Enhancing Instruction TLB Resilience to Soft Errors

Abstract: A translation lookaside buffer (TLB) is a type of cache used to speed up the virtual to physical memory translation process. Instruction TLBs store virtual page numbers and their related physical page numbers for the last accessed pages of instruction memory. TLBs like other memories suffer soft errors that can corrupt their contents. A false positive due to an error produced in the virtual page number stored in the TLB may lead to a wrong translation and, consequently, the execution of a wrong instruction that can lead to a program hard fault or to data corruption. Parity or error correction codes have been proposed to provide protection for the TLB, but they require additional storage space. This paper presents some schemes to increase the instruction TLB resilience to this type of errors without requiring any extra storage space, by taking advantage of the spatial locality principle that takes place when executing a program.

Autonomous storage management for low-end computing environments

Abstract: To make storage management transparent to users, enterprises rely on expensive storage infrastructure, such as high end storage appliances, tape robots, and offsite storage facilities, maintained by full-time professional system administrators. From the user's perspective access to data is seamless regardless of location, backup requires no periodic, manual action by the user, and help is available to recover from storage problems. The equipment and administrators protect users from the loss of data due to failures, such as device crashes, user errors, or virii, as well as being inconvenienced by the unavailability of critical files. Home users and small businesses must manage increasing amounts of important data distributed among an increasing number of storage devices. At the same time, expert system administration and specialized backup hardware are rarely available in these environments, due to their high cost. Users must make do with error-prone, manual, and time-consuming ad hoc solutions, such as periodically copying data to an external hard drive. Non-technical users are likely to make mistakes, which could result in the loss of a critical piece of data, such as a tax return, customer database, or an irreplaceable digital photograph. In this thesis, we show how to provide transparent storage management for home and small business users We introduce two new systems: The first, PodBase, transparently ensures availability and durability for mobile, personal devices that are mostly disconnected. The second, SLStore, provides enterprise-level data safety (e.g. protection from user error, software faults, or virus infection) without requiring expert administration or expensive hardware. Experimental results show that both systems are feasible, perform well, require minimal user attention, and do not depend on expert administration during disaster-free operation. PodBase relieves home users of many of the burdens of managing data on their personal devices. In the home environment, users typically have a large number of personal devices, many of them mobile devices, each of which contain storage, and which connect to each other intermittently. Each of these devices contain data that must be made durable, and available on other storage devices. Ensuring durability and availability is difficult and tiresome for non-expert users, as they must keep track of what data is stored on which devices. PodBase transparently ensures the durability of data despite the loss or failure of a subset of devices; at the same time, PodBase aims to make data available on all the devices appropriate for a given data type. PodBase takes advantage of storage resources and network bandwidth between devices that typically goes unused. The system uses an adaptive replication algorithm, which makes replication transparent to the user, even when complex replication strategies are necessary. Results from a prototype deployment in a small community of users show that PodBase can ensure the durability and availability of data stored on personal devices under a wide range of conditions with minimal user attention. Our second system, SLStore, brings enterprise-level data protection to home office and small business computing. It ensures that data can be recovered despite incidents like accidental data deletion, data corruption resulting from software errors or security breaches, or even catastrophic storage failure. However, unlike enterprise solutions, SLStore does riot require professional system administrators, expensive backup hard- ware, or routine, manual actions on the part of the user. The system relies on storage leases, which ensure that data cannot be overwritten for a pre-determined period, and an adaptive storage management layer which automatically adapts the level of backup to the storage available. We show that this system is both practical, reliable and easy to manage, even in the presence of hardware and software faults.

A Framework for Analyzing the Impact of Data Integrity/Quality on Electricity Market Operations

Abstract: This talk examines the impact of data integrity/quality in the supervisory control and data acquisition (SCADA) system on real-time locational marginal price (LMP) in electricity market operations. Measurement noise and/or manipulated sensor errors in a SCADA system may mislead system operators about real-time conditions in a power system, which, in turn, may impact the price signals in real-time power markets. This research serves as a first step to analytically investigate the impact of bad/malicious data on electric power market operations. The first part of this talk studies from a market participant’s perspective a new class of malicious data attacks on state estimation, which subsequently influences the result of the newly emerging look-ahead dispatch models in the real-time power market. We propose a novel attack strategy, named ramp-induced data (RID) attack, with which the attacker can manipulate the limits of ramp constraints of generators in look-ahead dispatch, leading to financial profits while being undetected by the existing bad data detection algorithm embedded in the state estimator. In the second part, we investigate from a system operator’s perspective the sensitivity of locational marginal price (LMP) with respect to data corruption-induced state estimation error in real-time power market. We present an analytical framework to quantify real-time LMP sensitivity subject to continuous and discrete data corruption via state estimation. The proposed framework offers system operators an online tool to identify economically sensitive buses and transmission lines to data corruption as well as find sensors that impact LMP changes significantly and influentially.