Dynamic Data Filtering for Secure Cloud Storage
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Solution Overview
Problem
Existing data storage and processing systems in household automation and body sensors face challenges such as unauthorized data modification, unauthorized data sharing, and data deletion, particularly when sensitive data is transferred to cloud services without user consent or control.
Innovation Solution
A system comprising a filter component, a filter management device, and a memory unit that allows for real-time filtering and storage of data without pre-defined filter rules, enabling users to control data processing and transmission by analyzing and structuring data content before sending it to a backend, with options for anonymization, format conversion, and metadata management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is transferred to cloud services for processing, then data processing capability is improved, but user control and data security deteriorate
Solution Approach 1:
The system segments data processing into multiple stages: local filtering at the edge device, selective transmission to cloud, and targeted cloud processing. This segmentation allows maintaining user control through local decision-making while still utilizing cloud processing capabilities for specific data subsets that pass the filter criteria.
Solution Approach 2:
The filter component acts as an intermediary between local data sources and cloud services. It evaluates data against filter rules before transmission, serving as a mediator that protects user control while enabling cloud processing. The filter management device further mediates by dynamically configuring filter rules based on data characteristics.
2Productivity
If filter rules are predefined, then filtering efficiency is improved, but adaptability to new data types deteriorates
Solution Approach 1:
The filter rules are made dynamic through the filter management device that can modify filter criteria based on analyzed data characteristics. The system transitions from static predefined rules to dynamic adaptive rules that evolve with new data types, maintaining both filtering efficiency and adaptability.
Solution Approach 2:
The filter management device automatically analyzes data streams and configures appropriate filter rules without requiring manual intervention. This self-service capability allows the system to adapt to new data types autonomously while maintaining efficient filtering through automatically optimized rules.
3Loss of information
If all data is transmitted to backend, then data availability is improved, but network bandwidth consumption and processing load deteriorate
Solution Approach 1:
Instead of transmitting all data to the backend, the system applies partial action by selectively filtering and transmitting only relevant data subsets that match filter criteria. This reduces network bandwidth consumption and backend processing load while maintaining data availability for transmitted portions.
Solution Approach 2:
The filter component extracts and transmits only specific data elements that meet filter rules, leaving irrelevant data locally. This extraction approach minimizes network bandwidth consumption and backend processing requirements while ensuring availability of critical data subsets.
Data Source
Figure 1a~1b
Figure 1c~1d
Figure 2~3
AI summary
The invention relates to a system for filtering and storing data comprising: a filter component (200); a filter management device (300); and a storage unit (500), wherein the filter component (200) is suitable for receiving data from a data generation device (100) and for receiving configuration data relating to at least one data stream and/or at least one data structure of the received data from the filter management device (300), and for sending at least a part of the received data to the storage unit (500), and for sending at least a part of the received data to a backend (600) using configuration data.