Privacy-Preserving Object Sensor With On-Device ML Detection
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Solution Overview
Problem
Existing sensors for presence detection suffer from low accuracy, inability to identify specific objects, and privacy vulnerabilities due to the capture and transmission of large amounts of extraneous data, which can compromise security and privacy.
Innovation Solution
A lightweight sensor using a machine-learned model for object detection that generates sensor signals based on model output, without storing or transmitting image data, and includes a tamper-resistant housing to ensure privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision-based systems stream all image data to a central location for object detection, then object detection capability is improved, but data privacy and security are compromised due to capture and transmission of large amounts of extraneous information
Solution Approach 1:
The patent extracts only the essential information needed for object detection (presence, count, type of objects) from the image data, while leaving the extraneous visual information local to the sensor. This is achieved by processing image data through a machine-learned model that outputs only detection results without transmitting the actual image content, thus extracting useful information while discarding privacy-sensitive data.
Solution Approach 2:
The patent introduces a machine-learned model as an intermediary between the image capture and data transmission stages. This intermediary processes the image data locally and converts it into anonymized detection results (object presence, count, classification) before any potential transmission, acting as a mediator that preserves privacy while maintaining detection functionality.
2Measurement precision
If sensors capture and transmit large amounts of image data for accurate detection, then detection accuracy is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent extracts only the necessary detection outcomes (object presence, count, type) from comprehensive image analysis, eliminating the need to store or transmit full image data. This extraction approach maintains detection accuracy while dramatically reducing the data volume that the sensor system must handle, thereby reducing device complexity and memory requirements.
Solution Approach 2:
The patent performs preliminary processing of image data through a machine-learned model directly at the sensor, converting raw image data into detection results before any further processing or transmission. This preliminary action eliminates the need for complex downstream processing systems and reduces the overall device complexity by performing the heavy computational lift at the source.
3Reliability
If continuous capture and storage of video streams is performed for detection tasks, then detection capability is improved, but vulnerability to security attacks increases and data privacy is reduced
Solution Approach 1:
The patent converts the potential harm of data exposure into a benefit by deliberately designing the system to capture only the minimum necessary information for detection (object presence, count, type) while excluding all extraneous visual data. This approach transforms what would be a security risk (storing/transmitting video streams) into a security advantage (minimal data footprint that is inherently more secure).
Solution Approach 2:
The patent employs transient memory for storing image data only temporarily during processing, with automatic deletion after extraction of detection results. This disposable approach to data storage ensures that sensitive visual information exists only briefly in memory and is never persisted, thereby eliminating long-term security vulnerabilities associated with stored video streams while maintaining detection capability.
Data Source
AI summary
In general, the disclosure is directed to lightweight sensors that enable privacy preserving detection of objects or related information present in image data. One aspect of the disclosure includes detection methods that do not require information such as motion or thermal in determining object detection. Rather, implementations can utilize a machine-learned model to determine whether objects of a type are present in an image. To account for possible generation of private data, example implementations can include hardware and/or a computing architecture configured to only maintain or transmit sensor signals generated based at least in part on output of the machine-learned model.