Network Trace Anonymization via Differential Privacy Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for anonymizing network trace data are vulnerable to injection attacks and fail to optimize the trade-off between privacy protection and utility preservation, leading to significant information loss and computational burdens.
Innovation Solution
A condensation-based differential privacy anonymization method that preserves privacy and utility by clustering network trace data features, adding Laplace noise to cluster means, and using prefix-preserving techniques for IP addresses, while maintaining efficient data analysis without additional computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network trace data is released to external entities for network management and analysis, then data utility and analysis efficiency are improved, but sensitive information such as IP addresses, user IDs, and authentication keys is exposed
Solution Approach 1:
The patent introduces anonymized network trace data as an intermediary between the original sensitive data and external analysis entities. The anonymization process creates a mediator representation that preserves analytical utility while blocking direct access to sensitive information through techniques like IP address generalization, timestamp perturbation, and flow aggregation
Solution Approach 2:
The patent creates anonymized copies of network trace data that replicate the structural and statistical properties needed for analysis without containing actual sensitive information. These copies serve as substitutes for the original data, allowing external entities to perform analyses on replicas rather than the genuine sensitive datasets
2Reliability
If traditional anonymization methods are applied to network trace data, then some privacy protection is achieved, but the data becomes vulnerable to injection attacks and utility is significantly reduced
Solution Approach 1:
The patent systematically modifies multiple parameters of network trace data simultaneously - IP addresses are generalized to different levels of hierarchy, timestamps are perturbed within acceptable ranges, packet sizes are aggregated into ranges, and flow durations are modified. These coordinated parameter changes achieve robust privacy protection while preserving the statistical relationships necessary for network analysis
Solution Approach 2:
The patent employs a composite anonymization approach that combines multiple protection techniques (IP generalization, timestamp perturbation, flow aggregation, packet size anonymization) into an integrated solution. This composite method creates a multi-layered protection mechanism that is more resilient to various attacks while maintaining data utility
3Reliability
If existing anonymization techniques are used, then some level of privacy is maintained, but the data requires significant computational overhead for processing and analysis
Solution Approach 1:
The patent performs anonymization processing in advance before data release, transforming the original network trace data into anonymized form ahead of time. This preliminary action eliminates the need for real-time computation during analysis operations, as external entities can directly query and analyze the pre-anonymized data without incurring additional computational overhead
Data Source
AI summary
The invention described herein is directed to methods and systems for protecting network trace data. Network traces are used for network management, packet classification, traffic engineering, tracking user behavior, identifying user behavior, analyzing network hierarchy, maintaining network security, and classifying packet flows. In some embodiments, network trace data is protected by subjecting network trace data to data anonymization using an anonymization algorithm that simultaneously provides sufficient privacy to accommodate the organization need of the network trace data owner, provides acceptable data utility to accommodate management and/or network investigative needs, and provides efficient data analysis, at the same time.