Pseudo-random Polymorphic Tree for Data Obfuscation
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Solution Overview
Problem
Existing encryption-based approaches for protecting sensitive data, such as cryptographic keys, are vulnerable to attacks and can be exploited, and traditional methods generate recognizable patterns that may reveal the data's location, compromising its secrecy.
Innovation Solution
The use of pseudo-random polymorphic trees, where a server generates a tag stream and data tree, combining them into a pseudo-random polymorphic tree that appears as random data, allowing the client to extract and re-hide the data securely, using a shared seed and byte-string algorithm, enhancing data secrecy by making it indistinguishable from random bytes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption-based approaches are used to protect sensitive data, then data security is improved, but the data generates recognizable patterns that reveal its location and can be exploited over time
Solution Approach 1:
The patent segments the data into multiple discrete elements and distributes them throughout the polymorphic tree structure. Each data element is separated by random padding and organized into hierarchical nodes, making the data invisible as a continuous recognizable pattern while maintaining extractability through the seed-based reconstruction process
Solution Approach 2:
The patent introduces a polymorphic tree structure as an intermediary between the original data and its storage/transmission form. This tree, generated from a shared seed, acts as a mediator that obscures the data's true nature and location while enabling authorized recovery through the same seed, thus hiding data patterns from attackers
2Reliability
If data is hidden within a polymorphic tree structure, then data secrecy is improved, but the system complexity increases due to seed management and tree construction
Solution Approach 1:
The patent creates a universal polymorphic tree structure that can accommodate any type of sensitive data (cryptographic keys, DRM content, credentials) through a single standardized approach. The seed-based generation method provides a multi-functional solution that handles data hiding, obfuscation, and secure transmission simultaneously across different applications and devices
Solution Approach 2:
The patent uses parameter changes in the form of seed values to dynamically generate different polymorphic tree structures. By varying the seed parameter, the same data can be hidden in completely different tree configurations, providing flexibility and adaptability without requiring complex custom solutions for each case
3Difficulty of detecting and measuring
If the pseudo-random polymorphic tree is made larger to obscure data patterns, then data obfuscation is improved, but the storage and transmission efficiency decreases
Solution Approach 1:
The patent applies partial action by using only the necessary amount of padding and tree structure required to achieve adequate obfuscation. Rather than excessively expanding the data size, the implementation uses minimal random elements just sufficient to hide patterns, balancing security with efficiency
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for obfuscating data via a pseudo-random polymorphic tree. A server, using a seed value shared with a client device, generates a tag stream according to a byte-string algorithm. The server passes the tag stream and the data to be transmitted to the client device through a pseudo-random polymorphic tree serializer to generate a pseudo-random polymorphic tree, which the server transmits to the client device. The client device, using the same seed and byte-string algorithm, generates the same tag stream as on the server. The client passes that tag stream and the received pseudo-random polymorphic tree through a pseudo-random polymorphic tree parser to extract the data. Data to be transmitted from the server to the client device is hidden in a block of seemingly random data, which changes for different seed values. This approach obfuscates data and has low processing overhead.