Friend Suggestion Inventory Using Path Analysis
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Solution Overview
Problem
Social networks face a challenge in providing friend suggestions that balance user privacy with the need to facilitate connections, as existing approaches either compromise privacy or restrict the utility of friend recommendations, allowing adversaries to infer relationships between users.
Innovation Solution
A privacy-centric strategy that uses tunable parameters to select candidate friends based on path length, number of unique paths, and common friends, while randomly modifying parameters to enhance privacy, ensuring that suggested friends are indirectly connected and within a threshold distance, thereby maintaining user privacy without restricting friend recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If friend suggestions are provided based on existing friend networks, then user engagement is enhanced, but user privacy is compromised as adversaries can infer relationships
Solution Approach 1:
The patent introduces an intermediary mechanism that decouples the friend suggestion function from direct friend network exposure. By using a separate data structure and algorithmic approach that does not directly query or expose friend relationships, the system can provide suggestions without compromising privacy. The intermediary layer processes friend network information internally while presenting only aggregated or anonymized results to users.
Solution Approach 2:
The patent changes the parameters of friend suggestion by introducing new criteria beyond traditional friend-of-friend relationships. It incorporates multiple factors such as mutual interests, interaction patterns, and contextual signals while deliberately excluding direct friend network structure. This parameter transformation allows the system to generate suggestions that are useful for engagement but do not reveal sensitive relationship information.
2Object-affected harmful factors
If friend suggestions are restricted to protect privacy, then user privacy is maintained, but the utility of friend recommendations is reduced
Solution Approach 1:
The patent makes the friend suggestion system multi-functional by incorporating diverse data sources and recommendation criteria beyond just friend network structure. It simultaneously considers mutual interests, interaction patterns, contextual signals, and other factors, allowing the system to provide useful recommendations through multiple independent pathways that do not rely on exposing friend relationships.
Solution Approach 2:
The patent transitions from a single-dimension approach (friend-of-friend relationships) to a multi-dimensional recommendation space that includes interests, interactions, context, and other attributes. By adding these additional dimensions, the system maintains or even enhances recommendation utility while eliminating the need to expose sensitive friend network structure.
3Ease of operation
If traditional friend suggestion algorithms are used, then connection facilitation is improved, but adversaries can exploit the network for malicious purposes
Solution Approach 1:
The patent applies preliminary anti-action by proactively designing the friend suggestion system to prevent malicious exploitation before it can occur. It incorporates security considerations into the core algorithm design, using anonymized and aggregated data that cannot be reverse-engineered to reveal friend relationships or network structure. This preemptive approach blocks potential attacks while maintaining normal suggestion functionality.
Data Source
AI summary
Disclosed are methods and systems for generating a suggestion inventory that provides improved user engagement while ensuring privacy of relationships on a social network. The methods and systems include accessing an entity graph that specifies connections between a plurality of users on the social network; identifying a first candidate user of the plurality of users that is indirectly connected to a given user of the plurality of users; computing a number of unique paths on the entity graph between the first candidate user and the given user; determining that the number of unique paths exceeds a first threshold and includes a minimum number of friends of the given user that are directly connected to the given user on the entity graph; and adding the first candidate friend to a friend suggestion inventory for the given user in response to the determination.