Data Masking via Compression Dictionaries
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Solution Overview
Problem
Multi-tenant database systems face challenges in ensuring data security and privacy while testing software and infrastructure, as using actual live customer data is legally and contractually prohibited, yet synthetic test data often fails to replicate real-world scenarios, leading to costly errors in production environments.
Innovation Solution
Implementing data masking via compression dictionaries, where customer data is compressed using a dictionary-based method and stored in a masked form, allowing for secure obfuscation and de-compression using a masked dictionary, enabling testing with sensitive data protection without exposing original customer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual live customer data is used for testing, then testing accuracy and reliability are improved, but data security and privacy compliance deteriorate
Solution Approach 1:
The patent creates masked copies of production data that preserve statistical properties and data relationships while obscuring sensitive information. The compression dictionary technique generates synthetic data copies that mimic real data patterns without containing actual customer information, enabling reliable testing while maintaining privacy compliance.
Solution Approach 2:
The patent introduces a compression dictionary as an intermediary layer between production data and test data. This dictionary acts as a mediator that transforms real customer data into masked representations, allowing test systems to access data-like structures without exposing sensitive information, thus resolving the conflict between testing reliability and data security.
2Object-affected harmful factors
If synthetic test data is used instead of real data, then data security is improved, but testing accuracy and real-world scenario replication deteriorate
Solution Approach 1:
The patent changes the parameters of test data by applying compression dictionary transformations that preserve statistical properties, data distributions, and relationships while altering the actual content. This allows synthetic data to maintain the structural characteristics of real data for accurate testing while ensuring security through parameter transformation.
3Object-affected harmful factors
If separate masked data copies are maintained for testing, then data privacy is protected, but storage costs and system complexity increase
Solution Approach 1:
The patent merges the functions of data masking and data storage by implementing masking directly within the compression dictionary structure. Instead of maintaining separate masked data copies, the system combines production and test data access through a unified compression dictionary mechanism, reducing storage requirements while maintaining privacy protection.
Solution Approach 2:
The compression dictionary serves multiple functions simultaneously: it acts as a data compression mechanism, a privacy protection layer, and a test data generation engine. This multi-functionality eliminates the need for separate masked data storage, reducing overall storage requirements while achieving both security and testing objectives.
Data Source
AI summary
In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for implementing data masking via compression dictionaries including, for example, means for receiving customer data at the host organization; compressing the customer data using dictionary based compression and a compression dictionary; storing the compressed customer data in a database of the host organization; retrieving the compressed customer data from the database of the host organization; and de-compressing the compressed customer data via a masked compression dictionary, in which the masked compression dictionary de-compresses the customer data into masked customer data. Other related embodiments are disclosed.