Decentralized Data Storage Fragmentation and Distribution
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Solution Overview
Problem
Current data storage solutions, particularly in cloud computing, face challenges in ensuring reliable and decentralized data storage and backup, which can lead to significant disruptions and monetary losses in case of data loss, as they often rely on centralized server farms and lack efficient decentralized and distributed data management systems.
Innovation Solution
A decentralized and distributed data storage system that uses a data storage engine to fragment data into multiple sets, storing these fragments across a multitude of end-user devices in a peer-to-peer network, allowing for dynamic allocation and encryption, with a challenge-response protocol for secure reconstruction of the original dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized server farms are used for cloud storage, then data accessibility is improved, but system reliability and security are worsened due to single points of failure
Solution Approach 1:
The patent segments data into multiple fragments and distributes them across numerous peer devices in a decentralized network. This fragmentation and distribution eliminates single points of failure while maintaining data accessibility through the peer-to-peer architecture, directly resolving the contradiction between centralized accessibility and decentralized reliability.
Solution Approach 2:
The patent transitions from a single-dimensional centralized storage model to a multi-dimensional decentralized network distributed across many peer devices. This dimensional shift allows the system to achieve both high accessibility (through the network interface) and high reliability (through distributed redundancy), resolving the fundamental trade-off.
2Reliability
If data is fragmented and distributed across peer devices, then system reliability is improved, but device complexity is worsened
Solution Approach 1:
The patent implements self-service mechanisms where peer devices automatically manage their own data fragment storage, validation, and retrieval operations. The system autonomously handles fragmentation, distribution, and reassembly without requiring complex centralized management, thereby achieving high reliability while keeping individual device complexity manageable.
Solution Approach 2:
The patent creates a universal peer-to-peer protocol that enables diverse peer devices to perform multiple functions (storage, validation, retrieval) through a standardized interface. This multi-functionality approach allows the system to achieve complex distributed reliability goals while maintaining simplicity at the individual device level through protocol standardization.
3Reliability
If challenge-response protocol is implemented for secure reconstruction, then data security is improved, but operation time is worsened due to additional verification steps
Solution Approach 1:
The patent performs preliminary actions by pre-establishing cryptographic credentials and validation rules during peer device registration and data fragment storage. When reconstruction is needed, the challenge-response protocol leverages these pre-configured elements to rapidly verify data integrity and authenticity, thereby enhancing security while minimizing the time penalty during actual reconstruction operations.
Data Source
AI summary
Aspects of embodiments relate to a system for decentralized and distributed storing of data. The system comprises an application provider operative to provide a data storage (DTS) engine that is configured to generate data fragments that are associated with a source dataset received at an end-user data source (DT) device. The DTS engine is also configured such that the generated data fragments are stored on a multitude of end-user DT devices. The DTS engine is further configured to reconstruct, based on the data fragments, the source dataset.