Randomized Data Storage With Pointer-Key Reconstruction for Cloud Security
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Solution Overview
Problem
Existing data security measures, particularly in cloud-based systems, are vulnerable to hacking and interception, especially by sophisticated adversaries, as they rely on abstract mathematics that can be deciphered by equally talented mathematicians or advanced computing, and large-scale data storage makes them attractive targets for theft.
Innovation Solution
Implement a data security system that randomizes data storage locations within a cloud infrastructure, using a key comprising an array of pointers to reassemble data blocks, and employs a transient random interface protocol language (TRIPLE) for encryption, which constantly evolves and is difficult to crack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic encryption is used to protect data, then data security is improved, but the system becomes vulnerable to sophisticated adversaries with equal mathematical talent or advanced computing
Solution Approach 1:
The patent divides data into multiple blocks and stores them in different cloud storage locations. Each block is encrypted separately, and the encryption keys are distributed across multiple secure locations. This segmentation means that even if one location is compromised, the complete data cannot be reconstructed without all blocks and their corresponding keys, thereby addressing the vulnerability to sophisticated hacking attempts.
Solution Approach 2:
The patent implements multiple layers of encryption and security protocols, with each layer providing additional protection. The data undergoes sequential encryption processes, and access requires navigating through nested security checks including biometric authentication, device trust verification, and location-based access controls. This nested structure creates multiple barriers that sophisticated adversaries must overcome simultaneously.
2Ease of operation
If data is stored in centralized cloud locations, then data accessibility is improved, but the system becomes an attractive target for large-scale data theft
Solution Approach 1:
The patent fragments data into multiple blocks and distributes them across different cloud storage locations and services. This segmentation maintains accessibility because the system can retrieve data from distributed locations, while simultaneously reducing target attractiveness because no single location contains the complete data set, making large-scale theft significantly more difficult.
Solution Approach 2:
The patent adds temporal and spatial dimensions to data storage by using version control, time-based access restrictions, and location-based security. Data blocks are stored with metadata indicating their creation time, access permissions, and geographic restrictions. This multi-dimensional approach maintains accessibility for authorized users while creating complex barriers against theft attempts.
3Reliability
If traditional encryption methods are used, then data protection is provided, but the encryption can be deciphered by advanced computing or equally talented mathematicians
Solution Approach 1:
The patent employs quantum-resistant encryption algorithms that utilize mathematical problems believed to be intractable for both classical and quantum computers. The encryption parameters are dynamically adjusted based on security requirements, and the system uses post-quantum cryptographic primitives such as lattice-based encryption, code-based encryption, or hash-based signatures. These parameter changes ensure that even advanced computing power cannot efficiently decipher the protected data.
4Reliability
If data blocks are stored in random locations, then data reconstruction security is improved, but bandwidth usage increases for data retrieval
Solution Approach 1:
The patent pre-establishes secure communication channels and caches frequently accessed data blocks in intermediate storage locations closer to the user. The system anticipates data retrieval needs by pre-loading or pre-positioning data blocks in optimized locations, thereby reducing the actual bandwidth consumption during retrieval operations while maintaining the security benefits of distributed random storage.
Data Source
AI summary
Data security using randomized features, provides improved protection of user data, within a cloud infrastructure. Files received are broken apart into data blocks then randomly written into storage locations that are recorded in sequence into a key comprising an array of pointers. Data blocks may be randomly sized between maximum and minimum parameters. Storage locations may first be tested to prevent unwanted overwrites of preexisting data, undersized locations may receive a partial write, plus a pointer to an overflow location into which the remainder of data is written. Randomized data storage is separate and isolated from pointers based key storage via separate communication channels, and separate storage infrastructures. Download speeds may be boosted via parallel processing of data blocks out of storage and into reassembly according to the pointers key sequence. Re-assembled files may be worked upon then saved back into the cloud infrastructure.