Datacule Structure for Tamper-Proof Data Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing blockchain systems face limitations in data integrity and security, as they rely on a linear chain structure that makes it difficult to manipulate data blocks without altering all subsequent blocks, lack options for altering or deleting data, and have position-dependent security levels, with the last block being particularly vulnerable.
Innovation Solution
A method for storing data using a multi-dimensional data block structure, referred to as a datacule, where data blocks can be linked to more than two neighboring blocks, allowing for bidirectional links and enhanced security through unique combination-dependent coefficients that meet predefined conditions, enabling secure data distribution in distributed IT systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a linear chain structure is used for data blocks, then data integrity is secured through unidirectional linking, but the security level becomes position-dependent and the last block remains vulnerable
Solution Approach 1:
The patent transitions from a one-dimensional linear chain structure to a multi-dimensional data block structure where blocks can be linked in multiple directions and dimensions. This allows any block to be connected to multiple other blocks, creating a network topology that eliminates the vulnerable end-points inherent in linear chains and distributes security uniformly across all blocks.
Solution Approach 2:
The patent introduces asymmetric linking mechanisms where forward and backward links between data blocks have different cryptographic properties. This asymmetry allows the system to maintain unidirectional integrity verification while enabling flexible manipulation and reorganization of blocks without compromising overall security, thus uniformizing the security level across all positions.
2Reliability
If unidirectional linking is used between data blocks, then manipulation of preceding blocks is detectable, but manipulation of following blocks cannot be detected by preceding blocks
Solution Approach 1:
By introducing multi-dimensional links, the patent enables bidirectional and multi-directional verification of data block integrity. Each block can verify its connections in multiple directions, providing comprehensive manipulation detection while maintaining the flexibility to reorganize and manipulate blocks as needed without breaking the verification chain.
3Reliability
If data blocks are permanently immutable, then data integrity is maintained, but the ability to alter or delete data is lost
Solution Approach 1:
The patent implements a dynamic data block structure where the immutability and manipulability of blocks can change over time based on system state and authorization. Blocks can transition between immutable and manipulable states, allowing the system to maintain integrity when needed while enabling necessary data alterations and deletions through controlled mechanisms that preserve overall cryptographic integrity.
4Ease of manufacture
If a linear chain structure is used, then the system is simple to implement, but flexible concatenation and manipulation of data blocks is limited
Solution Approach 1:
The patent extends the basic chain concept into multiple dimensions, allowing blocks to have multiple parents and children in a network structure. This maintains the conceptual simplicity of chaining while dramatically increasing manipulation flexibility, as blocks can be inserted, deleted, or reorganized in multiple directions without requiring complete restructuring of the entire data structure.
Data Source
AI summary
A method for storing data in a tamper-proof manner in a data block structure. The method includes, for a group of data blocks, determining functions, which are assigned to the data blocks of the group and dependent on the data stored in the corresponding data block; creating a combination of all functions assigned to the data blocks of the group; and determining a combination-dependent coefficient for each function of the combination, so that the combination meets a predefined condition; and for each data block of the group, determining a control group of data blocks of the group assigned to the corresponding data block; and storing the coefficient that was determined for the function of the corresponding data block in all data blocks of the control group.