Security Metadata Compression Without Message Expansion
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Solution Overview
Problem
Existing compression techniques are not effectively applicable to security-oriented compression due to their design for larger entities and elaborate dictionaries, leading to inefficiencies in storing and transmitting small-sized security metadata without message expansion, and the adoption of alternative methods like implicit integrity is slow to be accepted in the industry.
Innovation Solution
The proposed solution involves identifying patterns in small-sized data units such as nibbles, bytes, and words to compress security metadata, using techniques like byte-equality, adjacent byte-equality, and nibble-histogram-based compression to free up space for appending security metadata without message expansion, employing minimal dictionaries and efficient hardware implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If existing compression techniques are used on small-sized security metadata, then compression ratio is improved, but message expansion occurs preventing security metadata storage
Solution Approach 1:
The patent changes the fundamental parameters of compression by targeting specific small-sized patterns (nibbles, bytes, words) rather than applying general-purpose compression algorithms. This involves modifying the compression approach to work with fixed small units and specific pattern types, achieving space savings without the overhead that causes message expansion in traditional compression methods.
Solution Approach 2:
The patent segments the data into specific small units (nibbles, bytes, words) and applies compression rules to each segment type. By dividing the compression task into discrete unit-based segments rather than treating the entire message as one unit, the system achieves efficient compression of security metadata without requiring elaborate dictionaries or complex processing that would cause message expansion.
2Loss of substance
If traditional compression algorithms are applied to small data units, then compression effectiveness is improved, but computational overhead increases
Solution Approach 1:
The patent employs simple, lightweight compression rules that can be applied quickly and discarded, rather than complex algorithms requiring significant computational resources. The compression approach uses basic pattern matching on small units that can be executed with minimal overhead, making it suitable for hardware implementation in memory systems where resources are constrained.
Solution Approach 2:
The patent applies different compression rules to different local patterns within the data (e.g., repeated nibbles vs. repeated bytes vs. repeated words). By tailoring the compression approach to the specific local pattern being detected rather than applying a uniform complex algorithm, the system achieves effective space savings with minimal computational overhead.
3Loss of substance
If elaborate dictionaries are used in compression techniques, then compression ratio is improved, but hardware cost increases
Solution Approach 1:
The patent extracts and compresses only the essential repeating patterns at small units (nibbles, bytes, words) rather than maintaining elaborate dictionaries of all possible patterns. By taking out only the critical repeating elements that occur in security metadata and compressing those specifically, the system achieves good compression ratios without the hardware cost of implementing comprehensive compression dictionaries.
Data Source
AI summary
A method of data nibble-histogram compression can include determining a first amount of space freed by compressing the input data using a first compression technique, determining a second amount of space freed by compressing the input data using a second, different compression technique, compressing the input data using the compression technique of the first and second compression techniques determined to free up more space to create compressed input data, and inserting into the compressed input data, security data including one of a message authentication control (MAC) and an inventory control tag (ICT).