Venue Device Detection with Anonymization Server
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Solution Overview
Problem
Existing systems struggle to accurately determine if a mobile device is within a venue without requiring user action, and they face challenges in protecting personally identifiable information while analyzing foot traffic and content distribution effectiveness.
Innovation Solution
The system integrates data from a device detection system with anonymization techniques to determine if a device is within a venue, using wireless pings and machine learning models, while protecting user privacy by converting identifiers into anonymous forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS technology or social media check-ins are used to track foot traffic, then user location can be obtained, but user action is required and personally identifiable information is exposed
Solution Approach 1:
The system uses device detection technology to automatically detect and track mobile devices within venue boundaries without requiring any user action. The device detection system passively identifies devices based on their wireless signals, eliminating the need for users to enable GPS or interact with applications, thus resolving the contradiction between location detection accuracy and ease of operation
Solution Approach 2:
The system introduces an anonymization server as an intermediary between the device detection system and the content distribution system. This intermediary converts personally identifiable information into anonymous identifiers, allowing the system to track foot traffic and measure content effectiveness without exposing user privacy, thus resolving the contradiction between measurement precision and information protection
2Loss of information
If personally identifiable information is shared between content distribution systems and venue entities, then effectiveness of content distribution can be measured, but user privacy is compromised
Solution Approach 1:
The system extracts personally identifiable information from the data flow by using the anonymization server to convert user identifiers into anonymous identifiers. This extraction allows the system to retain and analyze the effectiveness data (whether users received content and visited the venue) while removing the harmful element of personal information exposure, thus resolving the contradiction between information availability and privacy protection
3Ease of operation
If device detection systems track foot traffic without user action, then ease of operation improves, but accuracy in determining venue presence decreases
Solution Approach 1:
The system performs preliminary action by establishing machine learning models trained on wireless signal characteristics before actual foot traffic measurement begins. These pre-trained models enable the system to accurately interpret device signals and determine venue presence without requiring user action, thus resolving the contradiction between ease of operation and measurement precision
Data Source
AI summary
A device detection system is configured to determine whether a device is located within a venue. The device detection system provides user identifiers associated with a device to an anonymization server. The anonymization server provides anonymous identifiers to the device detection system corresponding to the user identifiers. A content distribution system provides content items for a campaign associated with the venue to users. The content distribution system provides user identifiers to the anonymization server and receives anonymous identifiers from the anonymization server. The content distribution system provides the anonymous identifiers to the device detection system. The device detection system compares the sets of anonymous identifiers to identify users that received a content item and subsequently visited the venue.