Data Corruption

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

Poor man's watchpoints

Abstract: Bugs that result from corruption of program data can be very difficult to track down without specialized help from a debugger. If the debugger cannot help the user find the point at which data gets corrupted, the user may have a long iterative debugging task. If the debugger is able to stop execution of the program at the point where data gets corrupted, as with watchpoints (also known as data breakpoints), it may be a very simple task to find a data corruption bug. In this paper, we discuss a method of implementing watchpoints on a system without hardware watchpoint support. By instrumenting the program code to check memory accesses, and supplying an interface to the instrumentation in the debugger, we provide an efficient, general method of implementing watchpoints.

On testing persistent-memory-based software

Abstract: Leveraging Storage Class Memory (SCM) as a universal memory--i.e. as memory and storage at the same time--has deep implications on database architectures. It becomes possible to store a single copy of the data in SCM and directly operate on it at a fine granularity. However, exposing the whole database with direct access to the application dramatically increases the risk of data corruption. In this paper we propose a lightweight on-line testing framework that helps find and debug SCM-related errors that can occur upon software or power failures. Our testing framework simulates failures in critical code paths and achieves fast code coverage by leveraging call stack information to limit duplicate testing. It also partially covers the errors that might arise as a result of reordered memory operations. We show through an experimental evaluation that our testing framework is fast enough to be used with large software systems and discuss its use during the development of our in-house persistent SCM allocator.

Scalable asynchronous I/O testing tool

Abstract: Embodiments of the present invention generally provide a system and method for testing integrity of data transmitted to and from a target device through a data connection using an asynchronous I/O dispatch method. In one embodiment, the method includes generating a data load on the data connection by repetitively writing test data patterns to the target device and reading data patterns from the target device using an asynchronous I/O dispatch method, measuring data throughput to and from the target device while generating the data load, comparing the data patterns read from the target device to the test data patterns to detect data corruptions, and generating debug information if a data corruption is detected.

Method for detecting and correcting data errors in an RF data link

Abstract: Methods for detecting and correcting data errors in an RF data link include identifying valid data frames and corrupted data frames by measuring a data corruption level for each transmitted data frame, comparing the measured data corruption level for each corrupted data frame to a data corruption threshold, reconstructing the corrupted data frames having a data corruption level below the data corruption threshold, reconstructing the data block using data from valid and reconstructed data frames, and/or verifying the data in the reconstructed data block.

An Open Source Tool for Game Theoretic Health Data De-Identification.

Abstract: Biomedical data continues to grow in quantity and quality, creating new opportunities for research and data-driven applications. To realize these activities at scale, data must be shared beyond its initial point of collection. To maintain privacy, healthcare organizations often de-identify data, but they assume worst-case adversaries, inducing high levels of data corruption. Recently, game theory has been proposed to account for the incentives of data publishers and recipients (who attempt to re-identify patients), but this perspective has been more hypothetical than practical. In this paper, we report on a new game theoretic data publication strategy and its integration into the open source software ARX. We evaluate our implementation with an analysis on the relationship between data transformation, utility, and efficiency for over 30,000 demographic records drawn from the U.S. Census Bureau. The results indicate that our implementation is scalable and can be combined with various data privacy risk and quality measures.