Privacy-Preserving Cycle Detection in Directional Electronic Communications
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Solution Overview
Problem
Existing methods struggle to perform efficient and privacy-preserving graph analysis, particularly in directional electronic communications, due to the sensitivity and confidentiality of the data, which prevents parties from sharing their data for collaborative analysis.
Innovation Solution
The implementation of efficient, parallel, and privacy-preserving methods for graph analysis using multi-party computation techniques, allowing parties to construct a secret-shared union graph from their private data without revealing their data to each other, and perform graph analysis on this shared graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parties share their directional electronic communication data for collaborative graph analysis, then the quality and completeness of analysis improves, but data privacy and confidentiality are compromised
Solution Approach 1:
The patent introduces secret-sharing technology as an intermediary mechanism that allows multiple parties to collaboratively construct and analyze a union graph without directly sharing their private data. Each party's data is transformed into secret shares that can be processed collectively while maintaining individual privacy, thus resolving the contradiction between analysis quality and data privacy protection
2Loss of information
If conventional private graph analysis techniques are used, then data privacy is preserved, but analysis speed and efficiency are slow
Solution Approach 1:
The patent segments the graph analysis process into multiple parallel operations that can be executed simultaneously on secret-shared data. By dividing the analysis tasks and processing them in parallel while maintaining secret-sharing throughout the computation, the system achieves both privacy preservation and improved analysis speed, resolving the contradiction between privacy and productivity
Data Source
AI summary
Embodiments are directed to methods and systems that can be used to perform efficient, parallel, privacy-preserving graph analysis. One particular application of embodiments is performing private cycle detection in order to detect anomalous behavior in directional electronic communications. Two (or more) parties can each possess private electronic communication data, which can be used to construct a private directed union graph corresponding to the union of the parties' electronic communication data. This private union graph can be analyzed by a multi-party computation network in order to detect cycles of defined length (e.g., comprising between four and eight communicating participants). These cycles can be used as evidence of anomalous or illicit use of such electronic communications systems.