Adaptive Data Fragmentation Across Clouds for Secure Recovery
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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 stored across multiple cloud vendors, requiring a minimum number of shards to recover original data, minimizing exposure and resource usage through adaptive recursive descent and quantum fragmentation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional encryption methods (AES256) are used to protect data, then data security is improved, but data vulnerability increases because encryption keys provide points of weakness that hackers can exploit
Solution Approach 1:
The patent segments data into multiple encrypted shards distributed across different storage locations. Each shard alone is insufficient to reconstruct the original data, eliminating the vulnerability of single-point key compromise. The data is divided such that no single shard contains the complete information, making traditional key-based attacks ineffective.
Solution Approach 2:
The patent implements nested encryption where multiple layers of encryption are applied to data shards. Each shard is encrypted with its own key, and the reconstruction process requires combining multiple encrypted layers. This nested structure creates multiple defensive barriers that hackers must penetrate sequentially, significantly increasing attack complexity.
2Reliability
If data is copied or duplicated for redundancy, then data resilience is improved, but attack vector increases because multiple copies expose more data to potential hackers
Solution Approach 1:
Instead of creating complete copies of data for redundancy, the patent segments data into multiple shards and distributes them across different storage locations. Each shard represents only a portion of the total data, so even if multiple shards are compromised, the complete data remains protected. This segmentation approach provides resilience while minimizing the attack surface.
Solution Approach 2:
The patent applies different encryption keys and security measures to different shards based on their storage location and sensitivity. Each shard can have customized security properties tailored to its specific context, allowing for optimized security-resilience balance without uniformly increasing attack vectors across all data copies.
3Ease of repair
If multiple copies of data are stored for backup, then data recovery capability is improved, but carbon and financial costs increase due to redundant storage resources
Solution Approach 1:
The patent stores data in segmented shards across multiple locations rather than creating full redundant copies. This allows for efficient recovery by retrieving only the necessary shards from available storage locations, reducing the need for maintaining complete backup copies and thereby lowering storage resource requirements and associated costs.
Solution Approach 2:
The patent implements a threshold-based recovery mechanism where data can be recovered when a minimum number of shards (M out of N) are available, rather than requiring all shards or complete backup copies. This partial action approach enables recovery with fewer resources, reducing the carbon and financial costs of maintaining full redundancy while still ensuring data availability.
4Reliability
If data is fragmented and distributed across multiple cloud stores, then data privacy is improved, but system complexity increases due to management of distributed shards
Solution Approach 1:
The patent introduces a centralized coordination system that acts as an intermediary between users and the distributed shard storage network. This intermediary manages the complexity of shard distribution, tracking, and retrieval, while presenting a simplified interface to users. The mediator handles key management, shard location tracking, and reconstruction coordination, thereby maintaining data privacy through distribution while reducing perceived system complexity for end users.
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.