IoT Data Filtering via Privacy-Aware Segmentation
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Solution Overview
Problem
Conventional techniques for data processing in the IoT domain are inefficient as they either send all data for remote processing or process all data locally, failing to effectively handle sensitive information, which can compromise user privacy.
Innovation Solution
A novel technique that detects and separates sensitive data from non-sensitive data, processing the sensitive portions locally while sending the non-sensitive data for remote processing, and notifies the remote system about the partial nature of the data to ensure privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If all data is sent to cloud services for remote processing, then computational power and storage capacity are improved, but user privacy and data security deteriorate due to transmission of sensitive information
Solution Approach 1:
The patent segments data into sensitive and non-sensitive portions, and segments processing into local and remote components. Local processing handles sensitive data to protect privacy, while remote processing handles non-sensitive data to utilize cloud computational power, thereby resolving the contradiction between computational power and privacy protection.
Solution Approach 2:
The patent extracts sensitive data from the overall data set and processes it locally, separating it from non-sensitive data that can be processed remotely. This extraction approach allows the system to benefit from remote processing capabilities while protecting sensitive information, resolving the contradiction between computational power and privacy risk.
2Object-affected harmful factors
If all data is processed locally, then user privacy is protected, but processing efficiency and resource utilization deteriorate
Solution Approach 1:
The patent segments data into sensitive and non-sensitive portions, processing sensitive data locally to protect privacy while sending non-sensitive data to cloud services for efficient remote processing. This segmentation resolves the contradiction between privacy protection and processing efficiency by optimizing the processing location for each data type.
Solution Approach 2:
The patent applies different processing qualities to different data portions: local processing with high privacy protection for sensitive data, and remote processing with high computational efficiency for non-sensitive data. This local quality approach resolves the contradiction between privacy protection and processing efficiency.
3Power
If sensitive data is transmitted to cloud services, then remote processing capabilities are utilized, but data security and confidentiality worsen
Solution Approach 1:
The patent extracts sensitive data from the data set and processes it locally, preventing its transmission to cloud services. Only non-sensitive data is transmitted for remote processing, thereby maintaining data security while still utilizing remote processing capabilities for appropriate data.
4Object-affected harmful factors
If a hybrid local-remote processing approach is implemented, then privacy protection and processing efficiency are improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary classification of data into sensitive and non-sensitive portions before processing. This preliminary action simplifies the subsequent hybrid processing by clearly defining which data should be processed locally and which remotely, thereby reducing system complexity while maintaining privacy protection and processing efficiency.
Data Source
AI summary
A mechanism is described for facilitating smart filtering and local/remote processing of data according to one embodiment. A method of embodiments, as described herein, includes detecting data collected via one or more sensing components, and evaluating the collected data to identify one or more portions of the collected data having privacy relevance, where evaluating further includes classifying the one or more portions as private data and other portions of the collected as non-private data. The method may further include filtering out the private data from the non-private data of the collected data, and processing the private data, where the non-private data is transmitted to a remote computing device over a network.