Geolocation Metadata Suppression via Personal Place Classification
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Solution Overview
Problem
Users face challenges in controlling the sharing of geolocation metadata associated with photos, as current methods are laborious and error-prone, often leading to unintended sharing of private information, with public sharing being the default option on photo sharing services.
Innovation Solution
A method and apparatus that associate a representative geolocation with groups of images captured at the same location, determining the probability of future image captures at these locations, and suppressing metadata if the location is deemed personal, by either encrypting or storing it separately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually inspect and control geotag sharing for each photo, then privacy control is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary analysis of photo capture patterns, timestamps, and geolocation data to pre-identify personal places before the user needs to share photos. By analyzing historical capture data in advance, the system creates a database of personal locations, so when photo sharing is needed, the classification is already complete and ready, eliminating the need for manual inspection at sharing time.
Solution Approach 2:
The system automatically analyzes photo metadata, capture patterns, and geolocation information to self-classify personal places without requiring user intervention. The algorithm independently processes timestamp sequences, determines capture frequency, identifies recurring locations, and autonomously classifies personal places, making the system self-sufficient in the classification task.
2Ease of operation
If public sharing of geotags is set as default, then ease of operation is improved, but privacy risk increases
Solution Approach 1:
Instead of applying a uniform sharing policy to all photos, the system applies different sharing policies to different locations. Photos captured at classified personal places automatically receive restricted sharing policies, while photos from non-personal locations can use public sharing defaults. This location-specific differentiation allows public sharing to remain the default for most photos while automatically protecting personal locations.
Solution Approach 2:
The system pre-identifies personal places and pre-applies protective measures to photos captured at those locations before sharing occurs. By analyzing capture patterns and classifying personal places in advance, the system proactively prevents privacy risks rather than reacting to them after photos are shared, thereby maintaining ease of operation while reducing privacy exposure.
3Productivity
If automated classification of personal places is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the photo collection into distinct groups based on capture locations and patterns. By dividing photos into personal place categories and non-personal categories based on geolocation and timestamp analysis, the system processes each segment with appropriate sharing policies, improving overall productivity while managing complexity through modular classification.
Solution Approach 2:
The system uses changes in photo capture parameters (timestamps, geolocation coordinates, capture frequency intervals) to automatically classify personal places. By monitoring and analyzing these parameter variations, the system dynamically identifies personal locations without requiring complex user input, thereby improving processing efficiency while keeping the classification logic based on observable parameter changes.
Data Source
AI summary
Methods (105), apparatuses (600), and computer readable storage mediums for processing captured images having geolocations related to the captured images at the time of capture are disclosed. A representative geolocation is associated (110) with each group of images previously captured at the same location. For each representative geolocation, based on at least the timestamp of the images associated with the representative geolocation, the probability is determined (120) that a further image will be captured at or near the representative geolocation. For representative geolocations with a determined probability above a predetermined threshold, the respective representative geolocation is associated (130) with at least one personal place.