Abstracted Graphs for Privacy-Preserving Social Data Analysis
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Solution Overview
Problem
Social relationship graphs often contain private data that cannot be shared without permission, posing challenges in utilizing this data for business or social purposes while maintaining confidentiality.
Innovation Solution
A system generates abstracted graphs from social relationship graphs, providing summary statistics for permitted relationships while anonymizing or obscuring data from unpermitted sources, using techniques like salting with random data to protect confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw social relationship data is shared with requestors, then data usefulness and analytical value are improved, but privacy protection and confidentiality are worsened
Solution Approach 1:
The patent introduces an abstracted graph as an intermediary representation between the raw social relationship data and the requestor. This abstracted graph preserves useful statistical patterns and relationships while removing personally identifiable information and sensitive details, thereby enabling data utilization without direct exposure of private information.
Solution Approach 2:
The patent extracts only the essential statistical features and relationship patterns from the raw social relationship data, separating useful analytical information from sensitive personal data. This extraction process creates a simplified representation that maintains analytical value while eliminating privacy risks associated with raw data sharing.
2Object-affected harmful factors
If abstracted graphs with summary statistics are provided, then privacy protection is improved, but data completeness and analytical precision are worsened
Solution Approach 1:
The patent transforms the data representation by changing parameters from individual-level detailed information to aggregate-level statistical information. This parameter transformation maintains the essential analytical characteristics needed for business insights while reducing the granularity that exposes personal privacy, thus balancing privacy protection with data completeness.
3Adaptability or versatility
If selective abstraction is applied based on consent, then user control and ethical compliance are improved, but system complexity and processing overhead are worsened
Solution Approach 1:
The patent applies different levels of abstraction to different portions of the social relationship data based on user consent preferences. Specifically, data from users who have granted consent is presented in a non-abstracted manner, while data from users who have not consented is presented in an abstracted manner. This localized differentiation enables user control over their data while managing system complexity through consistent processing rules.
Data Source
AI summary
A system may generate abstracted graphs from a social relationship graph in response to a query. A query may identify a person for which permission has been obtains to collect their data. The abstracted graphs may include summary statistics for various relationships of the person. The relationships may include other persons, places, things, concepts, brands, or other object that may be present in a social relationship graph, and the relationships may be presented in an abstracted or summarized form. The abstracted form may preserve data that may be useful for the requestor, yet may prevent the requestor from receiving some raw data. When two or more people have given consent, the data relating to the consenting persons may be presented in a non-abstracted manner, while other data may be presented in an abstracted manner.