Anonymization of Network Traces Data through Condensation-based Differential Privacy

Abstract

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

“…The proposed method has a very high computational complexity, and privacy issues via graph linkage Heidari et al [70] Practical Strong privacy guarantees in graph data anonymity using k-edge-connected subgraph clustering Prone to identity and attribute disclosure in attributed social networks Wang et al [71] Conceptual Better privacy guarantees in publishing personal data using ρ uncertainty model Excessive disclosure of the sensitive transaction by not ensuring sufficient diversity in the SA's values Shyue et al [72] Practical Strong privacy protection in transactional data using sensitive k-anonymity with tuple delete/add Subject to important data items deletion that can hinder data analytics and mining Divanis et al [73] Practical Unified framework that satisfies multiple privacy requirements and incurs less IL Less applicability to heterogeneous data types, and sensitive itemset disclosure Awad et al [74] Theoretical Provides higher utility for certain itemset in transactional data using ant-based clustering The vulnerability analysis of selected itemset is not provided that may expose one/group privacy Awad et al [75] Practical Creates a neighbor dataset for knowledge discovery/extraction purposes using utility rules The vulnerability analysis of selected itemset is ignored that may impact one/group privacy Barakat et al [76] Practical Executed a privacy attack on k m -anonymity model that can expose the privacy of some user explicitly Utility analysis and formal proof of the privacy breach on large scale datasets are not provided WANG et al [77] Practical Sufficient protection for a group of people who have distinct privacy-related preferences in data Prone to lower utility on special-purpose metrics (e.g., accuracy, precision, recall, F1 scores, etc.) Can et al [78] Practical Ensures protection based on distinct privacy-related preferences provided by users to control anonymity Can lead to higher information loss if data is imbalanced, and values of most QIs are close Meisam et al [79] Practical Preserving both privacy and utility by creating k views of the trace data Can lead to higher computing cost when the dataset is large, utility can be poor when data is skewed Fan et al [80] Practical Effectively preserve the privacy of network flows data by creating synthetic data using GANs Can lead to higher utility loss when offset between original and synthetic data is high Meisam et al [81] Practical Preserves privacy of important fields in trace data using pseudonyms and Multiview approach Yields higher computing complexity by creating multiple views of data, and prone to linking attack Aleroud et al [82] Practical A DP-based prototype to address the privacy-utility trade-off in network trace data Subject to personal information disclosure in the presence of auxiliary information Ahmed et al [83] Practical Strong privacy protection of critical fields in network logs data using condensation-based approach Prone to low utility results on special purpose metrics (i.e., accuracy, F1, etc.) of data mining Velarde et al…”