Sensor-Based Location Sharing Control for Privacy Protection
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Solution Overview
Problem
Location-enabled devices, such as pet tracking devices, are vulnerable to misuse and privacy risks due to their ability to be concealed and tampered with, leading to unauthorized tracking of individuals or objects.
Innovation Solution
Implementing sensor-based privacy protection systems that use machine learning models and sensor data to determine the device's usage context, enabling or disabling location sharing based on predefined motion profiles and environmental conditions to prevent unauthorized tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If location sharing is enabled continuously, then location tracking functionality is maintained, but privacy security deteriorates due to potential unauthorized tracking
Solution Approach 1:
The patent implements dynamic location sharing control by transitioning from static continuous location sharing to dynamic conditional location sharing. The system monitors sensor data (accelerometer, microphone, camera) in real-time and adjusts location sharing status based on detected usage patterns. When normal usage is detected, location sharing remains enabled; when abnormal usage patterns are identified, location sharing is automatically disabled, thus maintaining reliability while reducing privacy security risks.
Solution Approach 2:
The patent employs feedback mechanisms by continuously monitoring sensor data and using machine learning models to analyze usage patterns. The system feeds sensor data (acceleration, sound, image) back into the decision-making process to determine whether to enable or disable location sharing. This closed-loop feedback system allows the device to adapt its location sharing behavior based on actual usage conditions, resolving the contradiction between maintaining functionality and ensuring security.
2Object-affected harmful factors
If sensor-based monitoring is implemented, then privacy protection is improved, but device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent applies multi-functionality by using existing sensors (accelerometer, microphone, camera) for multiple purposes. These sensors are originally designed for basic device functions but are repurposed to also monitor usage patterns for privacy protection. The accelerometer detects device orientation and motion patterns, the microphone captures ambient sounds, and the camera detects visual context. This approach improves privacy protection without requiring entirely new specialized sensors, thereby limiting the increase in device complexity.
Solution Approach 2:
The system implements self-service by using the device's own existing hardware resources (sensors, processors) to perform privacy monitoring and decision-making. Rather than requiring external monitoring systems or additional dedicated privacy protection hardware, the device autonomously monitors its own usage patterns and makes decisions about location sharing. This self-service approach minimizes additional complexity while achieving enhanced privacy protection.
3Measurement precision
If machine learning models are used for usage detection, then accuracy of unauthorized tracking detection is improved, but processing energy consumption increases
Solution Approach 1:
The patent applies partial action by using machine learning models selectively rather than continuously. The system processes sensor data through ML models only when needed for decision-making about location sharing status. The ML models analyze sensor data to detect abnormal usage patterns, and based on the detection results, the system determines whether to enable or disable location sharing. This partial application of complex processing reduces overall energy consumption while maintaining detection accuracy when required.
Data Source
AI summary
Devices and techniques are generally described for sensor-based privacy protection for devices. In some examples, a first machine learning model and first data generated by the accelerometer may be used to determine that the first data corresponds to a predefined motion profile. In various examples, a first location associated with the electronic device may be determined. In some further examples, the wireless transmitter may transmit second data indicating the first location based on the determining that the first data corresponds to quadruped movement.