Adaptive Data Fragmentation for Multi-Cloud Redundancy and Privacy
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Solution Overview
Problem
Conventional data security methods, such as AES256, are ineffective against hackers due to reliance on encryption keys and lack of redundancy, leading to data exposure and high recovery costs, especially in cloud storage environments where regulatory compliance and environmental impact are concerns.
Innovation Solution
A dynamic system that fragments data into secure, anonymized, and pseudonymized shards across multiple cloud vendors, using adaptive recursive descent and quantum fragmentation to ensure data resilience and privacy, minimizing storage needs and attack vectors while enabling fast recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored with conventional encryption methods (AES256), then data security is provided through encryption keys, but hackers can steal and decrypt the data, leading to data exposure
Solution Approach 1:
The patent segments encrypted data into multiple fragments or shards, distributing them across different storage locations. This segmentation ensures that hackers cannot access complete data even if they compromise one storage location, directly addressing the vulnerability of conventional encryption where a single key compromise leads to full data exposure
Solution Approach 2:
The patent introduces an intermediary layer between the encryption key and the data storage by using secret sharing schemes. The encryption key is divided into shares that are distributed across multiple locations, acting as intermediaries that prevent direct access to the key, thereby mitigating the harm of key theft
2Reliability
If multiple copies of data are created for redundancy, then data resilience against loss is improved, but the attack vector increases and carbon costs rise
Solution Approach 1:
Instead of creating multiple complete copies of data for redundancy, the patent segments the data into fragments and distributes them across multiple storage locations. This approach provides resilience against data loss while minimizing the attack vector, as hackers would need to compromise multiple distributed locations rather than targeting multiple identical copies
Solution Approach 2:
The patent applies different storage characteristics to different data fragments, storing them in diverse locations with different security profiles. This local differentiation optimizes the balance between resilience and security exposure, as each fragment's storage location can be tailored to its specific risk profile
3Reliability
If data is fragmented and distributed across multiple cloud vendors, then data privacy and resilience are enhanced, but system complexity increases
Solution Approach 1:
The patent employs a universal secret sharing scheme that can be applied across multiple cloud vendors and storage systems with a consistent methodology. This universal approach enhances data privacy and resilience through distributed fragmentation while managing system complexity by providing a standardized framework that can be implemented across diverse environments
Data Source
AI summary
Systems and methods for adaptive recursive descent data redundancy are described herein. In one embodiment, a method can include identifying the data object or file for Quantum Fragmentation, determining, via a first portion of a Quantum Fragmentation instance, a factor of fragmentation for the data object or file, transforming the data object or file into a plurality of first data fragments according to the factor of fragmentation by applying one or more cryptographic processing, integrity checking, and resilient fragmentation schemes, via the first portion of the Quantum Fragmentation instance, and persisting, via the first portion of the Quantum Fragmentation instance, each of the plurality of first data fragments to a data store of a plurality of available Cloud or other data stores or to a subsequent portion of the Quantum Fragmentation instance, wherein the persistence for each of the first data fragment occurs independently from the other first data fragments.