- Data Loss Prevention
- Data Loss Detection
- Data Classification, Labeling, and Tagging
- Obfuscation
- Anonymization
- Encrypted vs. Unencrypted
- Data Life Cycle
- Data Inventory and Mapping
- Data Integrity Management
- Data Storage, Backup, and Recovery
- Exam Preparation Tasks
- Review All Key Topics
- Define Key Terms
- Review Questions
This chapter is from the book
This chapter is from the book
This chapter is from the book
Obfuscation
Obfuscation is the act of making something obscure, unclear, or unintelligible. When we use that term with respect to sensitive or private information, it refers to changing the information in some way to make it unreadable to unauthorized individuals. It’s not encryption, however. In this section you’ll learn about methods of obfuscation.
Tokenization
Tokenization substitutes a sensitive value in data with another value that is not sensitive. It is an emerging standard for mobile transactions that uses numeric tokens to protect cardholders’ sensitive credit and debit card information. Tokenization is a great security feature that substitutes the primary account number with a numeric token that can be processed by all participants in the payment ecosystem.
Scrubbing
Data scrubbing actually has two meanings:
■ Scrubbing is used to maintain data quality. It involves checking main memory and storage for errors and making corrections using redundant data in the form of different checksums or copies of data. By detecting and correcting errors quickly, scrubbing reduces the likelihood that correctable errors will accumulate and lead to uncorrectable errors.
■ Scrubbing also can refer to removing private data. This meaning relates to obfuscation.
Masking
Data masking means altering data from its original state to protect it. You already learned about two forms of masking: encryption and hashing. Encryption is storing the data in an encrypted form, and hashing is storing a hash value (generated from the data by a hashing algorithm) rather than the data itself. Many passwords are stored as hash values.
Other methods of data hiding are
■ Using substitution tables and aliases for data
■ Redacting or replacing sensitive data with random values
■ Averaging or aggregating individual values