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Data Masking: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors

$39.95

Data Masking: High-impact Strategies - What You Need to Know: Definitions, Adoptions, Impact, Benefits, Maturity, Vendors

$39.95

Product Description

Data masking is the process of obscuring (masking) specific data elements within data stores. It ensures that sensitive data is replaced with realistic but not real data. The goal is that sensitive customer information is not available outside of the authorized environment. Data masking is typically done while provisioning non-production environments so that copies created to support test and development processes are not exposing sensitive information and thus avoiding risks of leaking. Masking algorithms are designed to be repeatable so referential integrity is maintained.

This book is your ultimate resource for Data Masking. Here you will find the most up-to-date information, analysis, background and everything you need to know.

Data masking is the process of obscuring (masking) specific data elements within data stores. It ensures that sensitive data is replaced with realistic but not real data. The goal is that sensitive customer information is not available outside of the authorized environment. Data masking is typically done while provisioning non-production environments so that copies created to support test and development processes are not exposing sensitive information and thus avoiding risks of leaking. Masking algorithms are designed to be repeatable so referential integrity is maintained.

Common business applications require constant patch and upgrade cycles and require that 6-8 copies of the application and data be made for testing. While organizations typically have strict controls on production systems, data security in non-production instances is often left up to trusting the employee, with potentially disastrous results.

Creating test and development copies in an automated process reduces the exposure of sensitive data. Database layout often changes, it is useful to maintain a list of sensitive columns in a without rewriting application code. Data masking is an effective strategy in reducing the risk of data exposure from inside and outside of an organization and should be considered a best practice for curing non-production databases.

This book is your ultimate resource for Data Masking. Here you will find the most up-to-date information, analysis, background and everything you need to know.

In easy to read chapters, with extensive references and links to get you to know all there is to know about Data Masking right away, covering: Data masking, Database management system, Comparison of database tools, Comparison of object-relational database management systems, ACID, ANSI-SPARC Architecture, Armstrong's axioms, Associative model of data, AutoNumber, Bidirectionalization, Big data, Bitemporal data, Block contention, Candidate key, Citrusleaf database, Column-oriented DBMS, Commit (data management), Comparison of relational database management systems, Connection pool, Correlation database, Create, read, update and delete, Cursor (databases), Data Control Language, Data Definition Language, Data Manipulation Language, Data mart, Data redundancy, Data retrieval, Data store, Database, Database administration and automation, Database design, Database dump, Database engine, Database model, Database normalization, Database storage structures, Database system, Database theory, Database transaction, Database trigger, Database tuning, Datasource, Deductive database, Distributed database management system, Document-oriented database, Enterprise database management, Expression index, Federated database system, Foreign key, Formatted File System, Heterogeneous Database System, Hierarchical query, In-database processing, In-memory database, Index (database), InfinityDB, Integrated Data Management, ISBL, Least number bits, Life cycle of a relational database, List of object database management systems, List of relational database management systems, Mariposa (database), Master data management, Metadatabase, Microsoft Access, MultiValue, Nested SQL, No-force, Object database, Object Exchange Model, Object-based spatial database, Object-relational database, Online transaction processing, Oracle Database, Oracle Exadata, Partial index, Partition (database), Polyinstantiation, Pool (computer science), Precedence graph, Probabilistic database, QUEL query languages, Query language, Query optimization, Query optimizer, Query plan, Quorum (distributed computing), Referential integrity, Materialized view, Relation (database), Synonym (database)...and much more.

This book explains in-depth the real drivers and workings of Data Masking. It reduces the risk of your technology, time and resources investment decisions by enabling you to compare your understanding of Data Masking with the objectivity of experienced professionals.

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