Decentralized Data Sharing for Low-Latency Privacy Control
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Solution Overview
Problem
Existing data sharing systems lack transparency and security, leading to potential misuse, theft, and unintended disclosure, with centralized data processing facing challenges in latency, privacy, and cyber resilience.
Innovation Solution
Decentralized data sharing systems where data objects are equipped with processing instructions and cryptographic tools, enabling edge computing to manage access, storage, and usage, ensuring data owners control over their data through encryption and processing instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If centralized data processing is used, then data storage and processing capacity is improved, but latency increases and privacy protection deteriorates
Solution Approach 1:
The patent segments the centralized data processing system into distributed edge computing nodes. Each edge device or gateway processes data locally rather than transmitting all data to a central server, reducing latency while maintaining processing capacity through parallel distributed computation.
Solution Approach 2:
The patent introduces a hierarchical architecture that adds a dimensional layer between centralized cloud and end devices - the edge computing layer. This intermediate layer processes data locally at network peripheries, reducing the distance data must travel and decreasing latency while preserving storage capacity through distributed nodes.
2Quantity of substance
If centralized data processing is used, then data storage and processing capacity is improved, but privacy protection deteriorates
Solution Approach 1:
The patent extracts sensitive data processing operations from the centralized cloud and places them at edge devices. By taking out processing functions from the central authority, the system reduces privacy risks associated with centralized data aggregation while maintaining necessary processing capacity through distributed computation.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary layers between data sources and centralized systems. These intermediaries process and filter data locally, acting as protective barriers that prevent direct exposure of sensitive data to centralized systems, thereby improving privacy protection while maintaining processing capacity.
3Quantity of substance
If centralized data processing is used, then data storage and processing capacity is improved, but cyber resilience deteriorates
Solution Approach 1:
The patent segments the monolithic centralized system into distributed edge computing instances. This segmentation ensures that a cyber-attack on one node does not compromise the entire system, improving cyber resilience while maintaining overall data processing capacity through continued operation of unaffected nodes.
Solution Approach 2:
The patent implements local processing capabilities at each edge node, enabling autonomous data processing decisions without requiring centralized control. This local quality approach improves cyber resilience by allowing nodes to continue functioning independently even when centralized systems are compromised, while collectively maintaining the necessary processing capacity.
4Ease of operation
If data sharing is implemented without decentralized control, then data accessibility is improved, but data security and control deteriorate
Solution Approach 1:
The patent enables data objects to have self-service capabilities through embedded processing instructions and autonomous execution. Data objects can independently manage their own access, sharing, and security without requiring centralized control, maintaining both accessibility and security through self-governance mechanisms.
Solution Approach 2:
The patent inverts the traditional centralized control model by embedding control capabilities directly within data objects themselves. Instead of central authorities controlling data access, the data objects autonomously manage their own sharing and security, improving both accessibility through direct data ownership and security through decentralized control.
Data Source
AI summary
An exemplary method comprises: transmitting, by a first edge node, a request for a data object to a web service; receiving, by the web service, the request for the data object; determining, by the web service, based on the request, an identity of a second edge node that has the data object, wherein the identity of the second edge node is determined from a database that associates data objects with edge nodes; transmitting, by the web service, a request to share the data object with the first edge node to the second edge node; and transmitting, by the second edge node, a copy of the data object to the first edge node based on the request from the web service.