Anonymized Cloud Group Data Sharing With Blockchain Access Control
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Solution Overview
Problem
Cloud-based data management platforms face challenges with data privacy, integrity, and scalability, leading to risks of data breaches, unauthorized usage, and inefficient resource utilization due to mistrust among participants, and inaccurate data affecting performance.
Innovation Solution
Integration of anonymized, member-driven cloud-based groups with content delivery services using distributed ledgers and permissioned blockchains, enabling secure data sharing and transaction management through smart contracts, where group-level data is collected without exposing individual member identities, and member-driven policies govern data access and compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based data management platforms collect and manage data transactions between multiple entities, then data-driven functions and actionable insights can be enhanced, but data privacy and security risks increase due to potential data breaches and unauthorized usage
Solution Approach 1:
The system segments data into individual-level data and group-level aggregated data. Individual members' data is kept private and anonymous, while only aggregated group-level insights are shared with content delivery services. This segmentation allows the system to maintain productivity through data-driven functions while protecting individual privacy and security.
Solution Approach 2:
The patent introduces an intermediary anonymization layer between individual members and content delivery services. This intermediary process transforms individual data into anonymous group-level aggregates, enabling data-driven insights while preventing unauthorized access to individual information and reducing security risks.
2Adaptability or versatility
If large-scale systems link thousands or more entities to enhance system scalability, then the platform can manage more data transactions, but the risk of losing track of sensitive data and maintaining data integrity increases
Solution Approach 1:
The system divides the large-scale network into independent cloud-based groups, each managing its own data transactions and privacy policies. This segmentation allows the system to scale to thousands of entities while maintaining data integrity through localized group-level control and anonymous aggregation, preventing loss of track of sensitive data.
Solution Approach 2:
The patent changes the parameter of data representation from individual-identifiable to group-anonymous aggregates. This parameter change enables the system to handle large-scale transactions while maintaining data integrity through consistent anonymization protocols and group-level policy enforcement.
3Productivity
If individual member data is collected for content delivery optimization, then targeted content delivery efficiency improves, but individual member identities may be compromised and data privacy violated
Solution Approach 1:
The system extracts only the necessary group-level aggregate information needed for content delivery optimization, while leaving individual member identities and sensitive information behind. This extraction approach enables efficient targeted content delivery without compromising individual privacy or violating data protection.
Solution Approach 2:
An anonymization intermediary process transforms individual member data into group-level aggregates that can be used for content delivery optimization without revealing individual identities. This intermediary layer maintains productivity through efficient targeting while preventing loss of individual identity privacy.
4Productivity
If cloud-based groups share data with external content delivery services, then actionable insights and targeted actions can be improved, but the risk of unauthorized data access and misuse increases
Solution Approach 1:
The system segments data sharing permissions at the group level, allowing cloud-based groups to control which aggregated data is shared with external services. This segmentation improves actionable insights quality through controlled data sharing while reducing unauthorized access risk through group-level policy enforcement and anonymous aggregation.
Data Source
AI summary
Techniques are described through which groups of individuals and/or other entities may interface with a data cloud blockchain network and/or cloud-based platform to collectively share data in a secure, controlled manner. Decentralized groups that are connected to the data cloud network may be registered and listed in a searchable directory. Entities that are interested in accessing data associated with a group may browse the directory, execute smart contracts within a blockchain, and track online content interactions of a group in a manner that does not compromise the anonymity of individual group members. Data usage and performance metrics may be tracked on the blockchain network using data cloud services, and the metrics may be written to distributed ledgers within the blockchain network. Smart contracts and chaincode within the network may initiate blockchain transactions based on performance metrics and/or other aspects associated with accessing information about a group.