Client-Side Geolocation Analytics for Privacy and Scalability
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Solution Overview
Problem
Current geolocation analytics systems face challenges in scaling and privacy, as they struggle to handle large amounts of data on client devices without revealing precise location information, leading to bandwidth and processing constraints, and existing solutions often leak sensitive data back to servers.
Innovation Solution
A distributed geolocation analytics system that shifts certain operations to client devices, using a client-side geolocation analytics application to request and process data about geographic areas with reduced resolution, allowing for anonymized data aggregation and obfuscation of device locations, thereby maintaining privacy and managing data volume effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geolocation analytics systems collect and process raw location data at scale on server-side platforms, then analytics accuracy and completeness are improved, but privacy protection deteriorates and data security risks increase
Solution Approach 1:
The patent extracts and removes personally identifiable location information from the data collection process. Instead of collecting raw GPS coordinates that can identify specific individuals, the system collects only aggregated geolocation analytics data that cannot be traced back to specific persons, thereby maintaining analytics value while protecting privacy
Solution Approach 2:
The patent merges individual geolocation data points into aggregated analytics data. By combining location information from multiple devices into collective behavioral patterns and trends, the system achieves meaningful analytics while the individual identities are lost in the aggregation, thus protecting privacy without sacrificing analytical accuracy
2Quantity of substance
If geolocation analytics systems process large amounts of detailed location data centrally, then data comprehensiveness is improved, but bandwidth consumption and processing demands increase
Solution Approach 1:
The patent extracts only the essential aggregated analytics data needed for insights, removing redundant raw location information. This selective data extraction reduces the volume of data transmitted over the network while retaining the core value needed for geolocation analytics
Solution Approach 2:
The patent segments the data processing function between client devices and server infrastructure. Client devices perform local processing to generate aggregated analytics, while the server receives only these processed results. This segmentation reduces network bandwidth consumption by eliminating the need to transmit large volumes of raw location data
3Measurement precision
If geolocation analytics systems store and access detailed geographic area data, then location context accuracy is improved, but data volume and storage requirements increase
Solution Approach 1:
The patent applies local quality by storing detailed geographic area data distributed across multiple client devices rather than centralizing it. Each device maintains relevant geographic context locally, allowing accurate location analysis without requiring all devices to store complete geographic datasets, thus reducing overall data volume while maintaining accuracy
Data Source
AI summary
Provided is a distributed application that shifts certain server-side operations from geolocation analytics platforms to client computing devices to enhance consumer privacy and the collection and use of potentially sensitive, personal data about an individual and their mobile device.