Edge Node Anonymization for Distributed Hierarchical Networks
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Solution Overview
Problem
Existing data anonymization techniques for distributed hierarchical networks, such as those in IoT, often introduce delays and result in significant information loss, making the shared data stale and less useful, particularly in high fan-in scenarios where real-time data is critical for privacy and utility.
Innovation Solution
A method where edge nodes in a distributed hierarchical network determine attribute hierarchy counts and share them to satisfy an anonymization criterion, allowing for real-time anonymization by generalizing and concealing private information, thereby reducing data emission delays and maintaining data utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anonymization techniques are employed to protect privacy, then data privacy is improved, but data emission delays increase and data becomes stale
Solution Approach 1:
The system performs preliminary anonymization processing at edge nodes before data leaves the network edge. By pre-computing anonymized aggregates and preparing data in advance, the system eliminates delays that would otherwise occur during centralized anonymization processing, thus maintaining both privacy and real-time data freshness
Solution Approach 2:
The anonymization process is segmented and distributed across multiple edge nodes rather than being centralized. Each edge node independently performs anonymization on its local data, allowing parallel processing and eliminating the bottleneck of centralized anonymization, thus reducing data emission delays while maintaining privacy
2Reliability
If traditional anonymization techniques are employed to protect privacy, then data privacy is improved, but information loss increases and data utility decreases
Solution Approach 1:
The system applies different anonymization strategies to different data types and contexts at edge nodes. By tailoring the anonymization level to local data characteristics and requirements, the system minimizes information loss while maintaining adequate privacy protection, preserving data utility for specific applications
Solution Approach 2:
The system applies anonymization only to the extent necessary to satisfy privacy requirements rather than applying blanket anonymization to all data. By performing partial anonymization on only the sensitive portions of data, the system maintains maximum data utility while achieving the required privacy protection level
3Adaptability or versatility
If data is shared across the network to enable collaboration, then network utility is improved, but privacy risks increase
Solution Approach 1:
Edge nodes act as intermediaries that perform anonymization processing between raw data collection and network-wide data sharing. This intermediary anonymization layer enables collaborative data sharing across the network while protecting individual privacy, as the shared data is already anonymized before leaving the edge node
Solution Approach 2:
Privacy protection through anonymization is performed in advance before data is shared across the network. By pre-anonymizing data at the edge, the system enables subsequent network-wide sharing and collaboration without exposing raw private information, thus improving network utility while maintaining privacy
Data Source
AI summary
Various implementations disclosed herein provide a method for anonymizing data in a distributed hierarchical network. In various implementations, the method includes determining a first set of attribute hierarchy counts that indicate a number of occurrences of corresponding attributes that are stored at the first network node and have not been transmitted upstream towards the hub. In various implementations, the method includes receiving, from a second network node, a second set of attribute hierarchy counts that indicate a number of occurrences of corresponding attributes at the second network node. In various implementations, the method includes determining whether a sum based on the first and second set of attribute hierarchy counts satisfies an anonymization criterion. In some implementations, the sum indicates a total number of occurrences for a corresponding attribute that are stored at the first and second network nodes and have not been transmitted upstream towards the hub.