Distributed Data Transformation With Zero-Knowledge Lineage Checks
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Solution Overview
Problem
Existing data management systems face challenges in securely transforming and verifying data without disclosing its contents, leading to information asymmetry and potential monopolies, which can stifle innovation and data value realization.
Innovation Solution
A peer-to-peer distributed file system using Contract Forest addressing, which identifies data by the sequence of parts used to generate it, allowing secure data transformation and verification without revealing the original data, and enables data to be stored and served close to consumers, reducing costs and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is encrypted and stored in secure data centers with layered security measures, then data security is improved, but information asymmetry increases and potential monopolies of knowledge are created
Solution Approach 1:
The patent segments data into multiple encrypted shares that are distributed across different nodes in a peer-to-peer network. No single node holds the complete data, preventing monopolies of knowledge while maintaining security through cryptographic segmentation of information.
Solution Approach 2:
The patent introduces cryptographic intermediaries (encryption schemes, zero-knowledge proofs, and trusted execution environments) that enable secure data transformation and verification without direct access to raw data. These intermediaries mediate between data owners and consumers, allowing information exchange while preserving security and preventing information asymmetry.
2Loss of information
If data is disclosed to various parties to reverse information asymmetry, then data value realization is improved, but data security and access control may be compromised
Solution Approach 1:
The patent extracts only the necessary information attributes from complete datasets, transforming data into aggregated statistics or derived insights that reverse information asymmetry without disclosing sensitive raw data. This extraction process maintains security while enabling value realization through selective information release.
Solution Approach 2:
The patent transforms data parameters through cryptographic operations and format-preserving transformations, changing the state of data from raw identifiable information to transformed representations that maintain utility for reversing information asymmetry while preserving security properties and access control.
3Loss of information
If data is transformed to a usable format for sale, then data value realization is improved, but original data format and security may be compromised
Solution Approach 1:
The patent performs preliminary cryptographic transformations and validations on data before it leaves the owner's control. Data is pre-processed into transformed formats with embedded verification mechanisms, ensuring both usability for commercial purposes and integrity protection through advance cryptographic preparation.
Solution Approach 2:
The patent creates cryptographic copies or representations of data that preserve essential characteristics for usability while maintaining security and integrity. These copies include format-preserving transformations and zero-knowledge proofs that verify data validity without revealing original sensitive information, enabling value realization without compromising source data.
Data Source
AI summary
This specification allows for secure, original data to be attributed, transformed attestably and managed within a data center or distributed across a peer to peer network using zero knowledge proofs that allow the holder of transformed data to verify, with certainty, that their transformed data was derived from the attributed data source, without visibility into the contents of that source data.