Location Data Retention Tables for Privacy-Preserving Granularity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Location-based applications face privacy concerns due to prolonged storage of player location data, which can lead to privacy breaches and inefficient data management, as well as the degradation of data usefulness over time.
Innovation Solution
Anonymizing location data by storing it in retention tables with varying levels of granularity and retention periods, progressively abstracting data to coarser levels, and deleting it after expiration, while ensuring privacy through hashed IDs and controlled access to raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If player location data is stored for prolonged periods for analysis purposes, then data availability for analysis is improved, but player privacy is compromised and database efficiency deteriorates
Solution Approach 1:
The patent segments location data into multiple retention tables with different retention periods and granularity levels. Fine-grained location data is stored in short-term tables for immediate analysis, while aggregated data is stored in long-term tables for historical analysis, eliminating the need to retain all detailed data indefinitely
Solution Approach 2:
The patent changes the granularity parameter of location data over time through periodic aggregation. Data transitions from fine-grained (individual player locations) to coarse-grained (aggregated regional statistics) as it moves between retention tables, maintaining analytical value while reducing privacy risk
2Measurement precision
If fine-grained location data is retained for long periods, then analytical precision is improved, but storage efficiency deteriorates and data staleness increases
Solution Approach 1:
The patent divides retention storage into multiple tables with different retention periods. Short-term retention tables store fine-grained data for immediate analysis, while long-term tables store aggregated data, allowing the system to query appropriate granularity based on analysis needs without storing all fine-grained data indefinitely
Solution Approach 2:
The patent implements periodic aggregation that discards fine-grained data after its retention period expires and recovers its analytical value by aggregating it into coarser-grained data for long-term retention tables, maintaining useful information while freeing storage space
Data Source
AI summary
A location-based application, such as a parallel reality game, may receive, from a client device of a user of the application, location data representing the user's position in the real-world. In one embodiment, a game platform associated with the application writes location data derived from the received location data to each of a plurality of retention tables, each storing the location data at a different level of granularity and for a different retention period. Upon expiration of a retention period for a first retention table, the game platform anonymizes the location data and transfers the anonymized data to a second retention table storing the data at a lower level of granularity. The game platform repeats this process until expiration of the retention period for the table having the lowest level of granularity before deleting the location data from storage.