Abstract:
Releasing private data can cause panic to both Online Social Network (OSN) users and service providers. Therefore, anonymization mechanisms are proposed to protect data b...Show MoreMetadata
Abstract:
Releasing private data can cause panic to both Online Social Network (OSN) users and service providers. Therefore, anonymization mechanisms are proposed to protect data before sharing it. However, some of these mechanisms set unrealistic privacy demands but cannot defend against real-world de-anonymization attacks.In this paper, we introduce an anonymization algorithm based on All-Distance Sketch (ADS). Sketching can significantly limit attackers’ confidence, as well as provide accurate estimation about shortest path length and other utility metrics. Because sketching removes large amounts of edges, it is invulnerable to seed-based and subgraph-based de-anonymization attacks. However, existing sketching algorithms do not add dummy edges and paths. Adversaries have low false positive in extracting linking information, which challenges the privacy performance. We propose the novel bottom-(l, k) sketch to defend against these advanced attacks. We develop a scheme to add and delete enough edges to satisfy our privacy demand. The experiment results show that our published graphs are closely matched with the original graphs under some metrics, preserving utility, while 80% edges are removed, ensuring privacy.
Published in: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
Date of Conference: 10-13 June 2019
Date Added to IEEE Xplore: 05 September 2019
ISBN Information:
ISSN Information:
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- IEEE Keywords
- Index Terms
- Online Social Network Data ,
- Shortest Path ,
- Shortest Path Length ,
- Original Graph ,
- Case Example ,
- Nodes In The Graph ,
- Privacy Protection ,
- Information Leakage ,
- Hash Function ,
- Clustering Coefficient ,
- Source Node ,
- Target User ,
- Simple Graph ,
- Breadth-first Search ,
- End Nodes ,
- Wrong Results ,
- Differential Privacy ,
- Part Of Path ,
- Average Shortest Path Length ,
- True Edges ,
- Existence Of Edges ,
- Facebook ,
- Edge Removal ,
- Specific Edge ,
- General Case ,
- Privacy Requirements ,
- Final Graph ,
- Average Confidence ,
- Average Path Length
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Online Social Network Data ,
- Shortest Path ,
- Shortest Path Length ,
- Original Graph ,
- Case Example ,
- Nodes In The Graph ,
- Privacy Protection ,
- Information Leakage ,
- Hash Function ,
- Clustering Coefficient ,
- Source Node ,
- Target User ,
- Simple Graph ,
- Breadth-first Search ,
- End Nodes ,
- Wrong Results ,
- Differential Privacy ,
- Part Of Path ,
- Average Shortest Path Length ,
- True Edges ,
- Existence Of Edges ,
- Facebook ,
- Edge Removal ,
- Specific Edge ,
- General Case ,
- Privacy Requirements ,
- Final Graph ,
- Average Confidence ,
- Average Path Length
- Author Keywords