Network Security Framework for Remote Data Quality Assessment
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Solution Overview
Problem
Consumers face challenges in verifying the suitability of data packages for intended purposes without exposing the data, while vendors are hesitant to reveal data packages pre-purchase, leading to a need for a solution that allows quality assessment without compromising data security.
Innovation Solution
A network security framework that enables remote users to assess data package quality using selected rules, blocking direct access until pre-defined conditions are met, utilizing a rule management service and data package management service to ensure data security and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data packages are exposed to consumers for quality verification, then consumers can assess data suitability, but data security and vendor protection are compromised
Solution Approach 1:
A rule management service is introduced as an intermediary between consumers and data packages. This service enables consumers to submit quality assessment rules without direct access to the data, while the service executes these rules on the data packages and returns only the quality scores. This mediator resolves the contradiction by allowing verification without exposure.
Solution Approach 2:
Instead of exposing the original data packages, the system creates virtual copies of quality assessment rules that can be executed on the data without transferring the data itself. The rules are copied and executed in a controlled environment, producing quality indicators without copying the actual data to the consumer.
2Object-affected harmful factors
If data packages are blocked from consumer access, then data security is maintained, but consumers cannot verify data quality before purchase
Solution Approach 1:
The rule management service acts as an intermediary that bridges the gap between data security requirements and quality verification needs. It receives quality assessment rules from consumers, executes them on secured data packages, and returns only the necessary quality information (scores) without exposing the underlying data, thus maintaining security while providing information.
Solution Approach 2:
The system extracts only the essential quality information from the data packages by executing assessment rules, while leaving the full data packages secured and inaccessible to consumers. This extraction of quality indicators without data transfer resolves the contradiction between security and information availability.
3Measurement precision
If comprehensive data quality assessment rules are implemented, then data quality evaluation accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The system allows consumers to select and submit only the specific quality assessment rules relevant to their needs rather than requiring comprehensive assessment of all possible data qualities. This partial action approach enables accurate evaluation for specific purposes while reducing overall system complexity and processing overhead.
Data Source
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AI summary
A method including receiving a request, via a network and from a remote user unauthorized to access a data package, for a quality score of the data package. A metric for evaluating the quality score is defined by the remote user. The method also includes receiving a rule. The rule is programmed to use, as input, the data package and generate, as output, the quality score. The rule is specified at least in part based on the metric. The method also includes generating the quality score by executing the rule on the data package. The method also includes transmitting, via the network, the quality score and a description of the data package while blocking access by the remote user to the data package.