Reliably Erasing Data from Flash-Based Solid State Drives
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An Analysis of Data Corruption in the Storage Stack
An Analysis of Data Corruption in the Storage Stack Lakshmi N. Bairavasundaram∗, Garth R. Goodson†, Bianca Schroeder‡ Andrea C. Arpaci-Dusseau∗, Remzi H. Arpaci-Dusseau∗ ∗University of Wisconsin-Madison †Network Appliance, Inc. ‡University of Toronto {laksh, dusseau, remzi}@cs.wisc.edu, [email protected], [email protected] Abstract latent sector errors, within disk drives [18]. Latent sector errors are detected by a drive’s internal error-correcting An important threat to reliable storage of data is silent codes (ECC) and are reported to the storage system. data corruption. In order to develop suitable protection Less well-known, however, is that current hard drives mechanisms against data corruption, it is essential to un- and controllers consist of hundreds-of-thousandsof lines derstand its characteristics. In this paper, we present the of low-level firmware code. This firmware code, along first large-scale study of data corruption. We analyze cor- with higher-level system software, has the potential for ruption instances recorded in production storage systems harboring bugs that can cause a more insidious type of containing a total of 1.53 million disk drives, over a pe- disk error – silent data corruption, where the data is riod of 41 months. We study three classes of corruption: silently corrupted with no indication from the drive that checksum mismatches, identity discrepancies, and par- an error has occurred. ity inconsistencies. We focus on checksum mismatches since they occur the most. Silent data corruptionscould lead to data loss more of- We find more than 400,000 instances of checksum ten than latent sector errors, since, unlike latent sector er- mismatches over the 41-month period. -
The 2006 Analysis of Information Remaining on Disks Offered for Sale on the Second Hand Market
Journal of Digital Forensics, Security and Law Volume 1 Number 3 Article 2 2006 The 2006 Analysis of Information Remaining on Disks Offered for Sale on the Second Hand Market Andy Jones Security Research Center, British Telecommunicationsand Edith Cowan University Craig Valli Edith Cowan University Iain Sutherland University of Glamorgan Paula Thomas University of Glamorgan Follow this and additional works at: https://commons.erau.edu/jdfsl Part of the Computer Engineering Commons, Computer Law Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, and the Information Security Commons Recommended Citation Jones, Andy; Valli, Craig; Sutherland, Iain; and Thomas, Paula (2006) "The 2006 Analysis of Information Remaining on Disks Offered for Sale on the Second Hand Market," Journal of Digital Forensics, Security and Law: Vol. 1 : No. 3 , Article 2. DOI: https://doi.org/10.15394/jdfsl.2006.1008 Available at: https://commons.erau.edu/jdfsl/vol1/iss3/2 This Article is brought to you for free and open access by the Journals at Scholarly Commons. It has been accepted for inclusion in Journal of Digital Forensics, Security and Law by an authorized administrator of (c)ADFSL Scholarly Commons. For more information, please contact [email protected]. Journal of Digital Forensics, Security and Law, Vol. 1(3) The 2006 Analysis of Information Remaining on Disks Offered for Sale on the Second Hand Market Andy Jones Security Research Center, British Telecommunications and Edith Cowan University [email protected] Phone: +44 1473 646133 Fax: +44 1473 644385 Craig Valli Edith Cowan University Iain Sutherland University of Glamorgan Paula Thomas University of Glamorgan ABSTRACT All organisations, whether in the public or private sector, use computers for the storage and processing of information relating to their business or services, their employees and their customers. -
How Blancco Helps Mobile Resellers & Recyclers Achieve Compliance
How Blancco Helps Mobile Resellers & Recyclers Achieve Compliance with the R2 Standard Mobile resellers and recyclers must comply with several standards and certifications before reselling, remarketing or recycling their devices. Following these rules helps mobile organizations ensure that they are protected from potential data remanence that could expose sensitive information. One of the most prominent certifications is called R2. The latest version of the Standard is the R2:2013. Each provision of the R2 Standard is designed to help ensure the transparency, quality, social and environmental responsibility of R2 Certified electronics facilities such as mobile resellers and recyclers. R2:2013 was developed through a transparent multi-stakeholder process, consistent with ANSI essential requirements. R2 undergoes consistent review to advance the requirements in-line with the needs of the industry. Blancco Mobile Diagnostics & Erasure solutions supply mobile resellers around the globe a certified process so that they can meet (and even exceed) the R2 standard for device testing and data sanitization. Blancco solutions exceed the requirements presented in the Standard, with verification and certification of each data erasure. This ensures that data does not remain on mobile devices following processing and prior to moving into the secondary market via reverse/forward logistics. Section of the Standard How Blancco Helps 1. Tested and Full Functions, R2/Ready for Reuse Blancco Mobile Solutions enables mobile resellers and retailers to (A) “Use effective test methods to confirm that all functions quickly and accurately find the source of mobile device issues and for equipment and components are working properly resolve them. and ready for reuse, including properly configured with appropriate legally licensed software where required for operation of equipment and components, and device specific drivers within the product’s hardware…” 2. -
Active Storage Activeraid ES Data Sheet
RAID Storage Evolved Welcome to RAID Storage Evolved Key Features Introducing the ActiveRAID ES — the affordable high performance RAID storage solution Optimized RAID performance in Apple Mac for Apple® users. The ActiveRAID ES was designed using the same principles of the OS X® Applications original ActiveRAID, but represents a previously unobtainable level of value by providing the capabilities of the ActiveRAID in a lower cost configuration that does not sacrifice Modern Linux RAID kernel and RAID the performance, reliability or ease of management of the original ActiveRAID. The controller design ActiveRAID ES was carefully designed for use in business critical applications where Proprietary self-optimizing architecture organizations require a high level of data integrity with ease of installation and management, but not the 24/7 365 day duty cycles of a large enterprise or Post-Production or Broadcast facility. Native Mac OS X Storage management suite The ActiveRAID ES provides redundant power, redundant cooling and redundant Fibre Dashboard Widget monitoring Channel connections, and the same powerful, yet easy to use management suite of the original ActiveRAID. The ES differs from the original ActiveRAID in that it has a single high Enterprise performance and reliability performance RAID controller and uses business class hard drives with a smaller cache size. without Enterprise complexity This results in a very robust RAID solution ideal for business class applications. Performance No long Quick Start guides to memorize of the ActiveRAID ES is impressive at up to 774 MB/s*† throughput and will easily handle Performance profiling and statistical most Mac OS X Server applications. The ActiveRAID ES is the ideal storage for business planning tools class applications such as File serving, database services, web hosting, calendar, Wiki, Mail, iChat and much more. -
Algorithms for Data Cleaning in Knowledge Bases
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan) VFAST Transactions on Software Engineering http://vfast.org/journals/index.php/VTSE@ 2018, ISSN(e): 2309-6519; ISSN(p): 2411-6327 Volume 15, Number 2, May-August, 2018 pp. 78-83 ALGORITHMS FOR DATA CLEANING IN KNOWLEDGE BASES 1 1 1 ADEEL ASHRAF , SARAH ILYAS , KHAWAJA UBAID UR REHMAN AND SHAKEEL AHMAD2 ` 1Department of Computer Science, University of Management and Technology, Punjab, Pakistan 2Department of Computer Sciences, Faculty of Computing and Information Technology in rabigh, King Abdulaziz University Jeddah Email: [email protected] ABSTRACT. Data cleaning is an action which includes a process of correcting and identifying the inconsistencies and errors in data warehouse. Different terms are uses in these papers like data cleaning also called data scrubbing. Using data scrubbing to get high quality data and this is one the data ETL (extraction transformation and loading tools). Now a day there is a need of authentic information for better decision-making. So we conduct a review paper in which six papers are reviewed related to data cleaning. Relating papers discussed different algorithms, methods, problems, their solutions and approaches etc. Each paper has their own methods to solve a problem in efficient way, but all the paper have common problem of data cleaning and inconsistencies. In these papers data inconsistencies, identification of the errors, conflicting, duplicates records etc problems are discussed in detail and also provided the solutions. These algorithms increase the quality of data. -
A Study Over Importance of Data Cleansing in Data Warehouse Shivangi Rana Er
Volume 6, Issue 4, April 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Study over Importance of Data Cleansing in Data Warehouse Shivangi Rana Er. Gagan Prakesh Negi Kapil Kapoor Research Scholar Assistant Professor Associate Professor CSE Department CSE Department ECE Department Abhilashi Group of Institutions Abhilashi Group of Institutions Abhilashi Group of Institutions (School of Engg.) (School of Engg.) (School of Engg.) Chail Chowk, Mandi, India Chail Chowk, Mandi, India Chail Chowk Mandi, India Abstract: Cleansing data from impurities is an integral part of data processing and maintenance. This has lead to the development of a broad range of methods intending to enhance the accuracy and thereby the usability of existing data. In these days, many organizations tend to use a Data Warehouse to meet the requirements to develop decision-making processes and achieve their goals better and satisfy their customers. It enables Executives to access the information they need in a timely manner for making the right decision for any work. Decision Support System (DSS) is one of the means that applied in data mining. Its robust and better decision depends on an important and conclusive factor called Data Quality (DQ), to obtain a high data quality using Data Scrubbing (DS) which is one of data Extraction Transformation and Loading (ETL) tools. Data Scrubbing is very important and necessary in the Data Warehouse (DW). This paper presents a survey of sources of error in data, data quality challenges and approaches, data cleaning types and techniques and an overview of ETL Process. -
Design Tradeoffs for SSD Reliability Bryan S
Design Tradeoffs for SSD Reliability Bryan S. Kim, Seoul National University; Jongmoo Choi, Dankook University; Sang Lyul Min, Seoul National University https://www.usenix.org/conference/fast19/presentation/kim-bryan This paper is included in the Proceedings of the 17th USENIX Conference on File and Storage Technologies (FAST ’19). February 25–28, 2019 • Boston, MA, USA 978-1-939133-09-0 Open access to the Proceedings of the 17th USENIX Conference on File and Storage Technologies (FAST ’19) is sponsored by Design Tradeoffs for SSD Reliability Bryan S. Kim Jongmoo Choi Sang Lyul Min Seoul National University Dankook University Seoul National University Abstract SSD Redun- Data dancy mgmt. Flash memory-based SSDs are popular across a wide range scheme of data storage markets, while the underlying storage medium—flash memory—is becoming increasingly unreli- SSD reliability Flash Data Data Host enhancement able. As a result, modern SSDs employ a number of in- memory re-read scrubbing system techniques device reliability enhancement techniques, but none of them RBER of UBER offers a one size fits all solution when considering the multi- Thres. Error underlying requirement for voltage correction dimensional requirements for SSDs: performance, reliabil- memories: SSDs: tuning code ity, and lifetime. 10-4 ~ 10-2 10-17 ~ 10-15 In this paper, we examine the design tradeoffs of exist- Figure 1: SSD reliability enhancement techniques. ing reliability enhancement techniques such as data re-read, intra-SSD redundancy, and data scrubbing. We observe that an uncoordinated use of these techniques adversely affects ory [10]. The high error rates in today’s flash memory are the performance of the SSD, and careful management of the caused by various reasons, from wear-and-tear [11,19,27], to techniques is necessary for a graceful performance degrada- gradual charge leakage [11,14,51] and data disturbance [13, tion while maintaining a high reliability standard. -
Data Remanence in Flash Memory Devices
Data Remanence in Flash Memory Devices Sergei Skorobogatov University of Cambridge, Computer Laboratory, 15 JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom [email protected] Abstract. Data remanence is the residual physical representation of data that has been erased or overwritten. In non-volatile programmable devices, such as UV EPROM, EEPROM or Flash, bits are stored as charge in the floating gate of a transistor. After each erase operation, some of this charge remains. Security protection in microcontrollers and smartcards with EEPROM/Flash memories is based on the assumption that information from the memory disappears completely after erasing. While microcontroller manufacturers successfully hardened already their designs against a range of attacks, they still have a common problem with data remanence in floating-gate transistors. Even after an erase operation, the transistor does not return fully to its initial state, thereby allowing the attacker to distinguish between previously programmed and not programmed transistors, and thus restore information from erased memory. The research in this direction is summarised here and it is shown how much information can be extracted from some microcontrollers after their memory has been ‘erased’. 1 Introduction Data remanence as a problem was first discovered in magnetic media [1,2]. Even if the information is overwritten several times on disks and tapes, it can still be possible to extract the initial data. This led to the development of special methods for reliably removing confidential information from magnetic media. Semiconductor memory in security modules was found to have similar prob- lems with reliable data deletion [3,4]. Data remanence affects not only SRAM, but also memory types like DRAM, UV EPROM, EEPROM and Flash [5]. -
What Is Data Preparation?
Analytics Canvas Tutorial: What is Data Preparation? nModal Solutions Inc. All Rights Reserved Analytics Canvas Tutorial: Course Introduction What is Data Preparation? Myth: Data preparation is a time-consuming manual process that is heavily prone to errors. Fact: Data preparation, when done with the right tools, empowers users to maintain control, while accelerating the process and providing a better foundation for decision-making. Better decision- making starts with better data. To an extent, data preparation is synonymous with, or related to: data pre-processing, data scrubbing, business intelligence, data cleansing/cleaning, and ETL (“Extract, Transform, and Load”). In this course, we are going to take a broad view of data preparation. Here is the definition we will work with: data preparation is a step in the data analysis process, in which data from one or more sources is cleaned, and transformed and enriched to improve its quality prior to its use. Data- driven decisions Knowledge discovery Data Science Descriptive analytics and reporting Data preparation Data Sources Figure 1: Data Preparation in the Data Analysis Process The figure above illustrates the position of the data preparation in the data analysis process. It is easy to see the importance of data preparation - as the foundation of business decisions. nModal Solutions Inc. All Rights Reserved Analytics Canvas Tutorial: Course Introduction Conclusion Thank you for reading this tutorial. We invite you to continue with the tutorial training to learn more about using Analytics Canvas. nModal Solutions Inc. All Rights Reserved . -
Data Storage on Unix
Data Storage on Unix Patrick Louis 2017-11-05 Published online on venam.nixers.net © Patrick Louis 2017 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of the rightful author. First published eBook format 2017 The author has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Introduction 4 Ideas and Concepts 5 The Overall Generic Architecture 6 Lowest Level - Hardware & Limitation 8 The Medium ................................ 8 Connectors ................................. 10 The Drivers ................................. 14 A Mention on Block Devices and the Block Layer ......... 16 Mid Level - Partitions and Volumes Organisation 18 What’s a partitions ............................. 21 High Level 24 A Big Overview of FS ........................... 24 FS Examples ............................. 27 A Bit About History and the Origin of Unix FS .......... 28 VFS & POSIX I/O Layer ...................... 28 POSIX I/O 31 Management, Commands, & Forensic 32 Conclusion 33 Bibliography 34 3 Introduction Libraries and banks, amongst other institutions, used to have a filing system, some still have them. They had drawers, holders, and many tools to store the paperwork and organise it so that they could easily retrieve, through some documented process, at a later stage whatever they needed. That’s where the name filesystem in the computer world emerges from and this is oneofthe subject of this episode. We’re going to discuss data storage on Unix with some discussion about filesys- tem and an emphasis on storage device. -
Lustre / ZFS at Indiana University
Lustre / ZFS at Indiana University HPC-IODC Workshop, Frankfurt June 28, 2018 Stephen Simms Manager, High Performance File Systems [email protected] Tom Crowe Team Lead, High Performance File Systems [email protected] Greetings from Indiana University Indiana University has 8 Campuses Fall 2017 Total Student Count 93,934 IU has a central IT organization • UITS • 1200 Full / Part Time Staff • Approximately $184M Budget • HPC resources are managed by Research Technologies division with $15.9M Budget Lustre at Indiana University 2005 – Small HP SFS Install, 1 Gb interconnnects 2006 – Data Capacitor (535 TB, DDN 9550s, 10Gb) 2008 – Data Capacitor WAN (339 TB, DDN 9550, 10Gb) 2011 – Data Capacitor 1.5 (1.1 PB, DDN SFA-10K, 10Gb) PAINFUL MIGRATION using rsync 2013 – Data Capacitor II (5.3 PB, DDN SFA12K-40s, FDR) 2015 – Wrangler with TACC (10 PB, Dell, FDR) 2016 – Data Capacitor RAM (35 TB SSD, HP, FDR-10) 2016 DC-WAN 2 We wanted to replace our DDN 9550 WAN file system • 1.1 PB DC 1.5 DDN SFA10K • 5 years of reliable production, now aging • Recycled our 4 x 40 Gb Ethernet OSS nodes • Upgrade to Lustre with IU developed nodemap • UID mapping for mounts over distance We wanted to give laboratories bounded space • DDN developed project quota were not available • Used a combination of ZFS quotas and Lustre pools We wanted some operational experience with Lustre / ZFS Why ZFS? • Extreme Scaling • Max Lustre File System Size • LDISKFS – 512 PB • ZFS – 8 EB • Snapshots • Supported in Lustre version 2.10 • Online Data Scrubbing • Provides error detection and -
Data Governance Defined
Data Governance Defined Katherine Downing, Vice President AHIMA ©2018 ©2018 Objectives for this Session • Define Data Governance • Examine the urgent drivers for Data Governance • Describe DG key functions, including master data and metadata management • Initiate planning for enterprise data governance ©2018 More: Data Everywhere • Applications • Medical Devices • Consumer Devices • Systems ©2018 What is Data? • The dates, numbers, images, symbols, letters, and words that represent basic facts and observations about people processes, measurements, and conditions • Raw facts without meaning or context American Health Information Management Association. Pocket Glossary of Health Information Management and Technology. 5th Edition. AHIMA Press. 2017. ©2018 Data Continues Expand • Dark Data • Non-relational data stores • Blockchain • Machine Learning and Artificial Intelligence • Data Lakes • And many others ©2018 Value of Data Data in its raw form is of limited value. It needs to be processed in a meaningful way to create information that would be relevant to a situation. ©2018 Defining Information Governance ©2018 AHIMA’s Information Governance Adoption Model (IGAM™) ➢ 10 competencies ➢ 75+ maturity markers ➢ 1 IG program ➢ All of healthcare ©2018 IGAM™ is available through www.IGHealthRate.com WHAT IS DATA GOVERNANCE (DG)? Data Governance (DG) provides for the design and execution of data needs planning and data quality assurance in concert with the strategic information needs of the organization. Data Governance includes data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries. ©2018 Why Data Governance? “If we lose, can’t find, can’t understand, or can’t integrate valuable enterprise data or if it is of questionable quality- the maximum value of the data is not realized, leading to a loss of business insight or sub-optimal operations.