Terminal Cloud Data Segmentation for Privacy and Resource Optimization
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Solution Overview
Problem
Current cloud computing methods are inadequate for handling the increasing data generated by IoT devices, as they lead to excessive exposure of personal information, inefficient resource utilization, and lack adaptability in expanding system capacity to meet varying user data needs.
Innovation Solution
A terminal and cloud apparatus system that cooperatively processes data by classifying information into hierarchical layers, allowing users to set sharing restrictions, and distributing data processing between the terminal and cloud based on these layers, ensuring privacy protection and adaptive resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is centralized on cloud apparatus for analysis, then service capability is improved, but user privacy is compromised due to excessive exposure of personal information
Solution Approach 1:
The patent segments data into multiple hierarchical layers (first layer through fourth layer) with different privacy levels. Each layer can be independently processed and shared with the cloud apparatus according to user-defined policies, allowing selective data exposure that maintains privacy while enabling service functionality.
Solution Approach 2:
Different layers of data are assigned different privacy protection levels and sharing policies. The terminal apparatus locally determines which layers to share based on user preferences, allowing high-level anonymized data to be shared while keeping sensitive personal information local, thus achieving localized privacy protection.
2Measurement precision
If data processing is performed only on cloud apparatus, then analysis performance is improved, but operating expenses increase and system expandability is limited
Solution Approach 1:
The patent implements dynamic workload distribution where processing tasks are flexibly allocated between terminal apparatus and cloud apparatus based on real-time conditions. The terminal can perform local processing for simple tasks while offloading complex analysis to the cloud, and this distribution can be dynamically adjusted based on network status, battery level, and data sensitivity.
3Device complexity
If resources are statically distributed between terminal and cloud, then system simplicity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system dynamically adjusts resource allocation between terminal and cloud based on real-time conditions such as network availability, battery status, data sensitivity, and processing requirements. This allows the system to optimize resource utilization for each specific task while maintaining a relatively simple overall architecture.
4Reliability
If data is offloaded only from terminal to cloud, then data security is improved, but system adaptability decreases for handling varying user data needs
Solution Approach 1:
The patent implements bidirectional and flexible data flow where data can be selectively shared from terminal to cloud or processed locally based on user-defined privacy policies. The hierarchical layer structure allows the system to adapt to varying data needs by adjusting which layers are shared, enabling scalable deployment from single-user to multi-user scenarios.
Data Source
AI summary
A terminal, a cloud apparatus, a method of analyzing, by a terminal, activities of a user, and a method of analyzing, by a cloud apparatus, activities of a user. The terminal includes a communication interface that communicates with an external apparatus over a network; a controller that obtains data used to predict activities of a user and anonymize a portion of the obtained data, and transmit the anonymized data and a remaining portion of the data, which is not anonymized, to the external apparatus through the communication interface; and a display that displays notification information related to the activities of the user based on activity prediction data received from the external apparatus, the activity prediction data being generated based on an analysis of the transmitted data.