The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks
Abstract
Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary's prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.
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Cited by
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793
Progress in Outlier Detection Techniques: A Survey
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A survey on security and privacy issues of blockchain technology
Prashanth Joshi, A., K. S. U., Han, M., & Wang, Y. (2018) · Mathematical Foundations of Computing · doi:10.3934/mfc.2018007 · Cited by 362
“The spectrum of blockchain applications range from financial, healthcare, automobile, risk management, Internet of things (IoT) to public and social services.”
BackgroundInfluentialCollaborative city digital twin for the COVID-19 pandemic: A federated learning solution
Pang, J., Huang, Y., Xie, Z., Li, J., & Cai, Z. (2021) · Tsinghua Science and Technology · doi:10.26599/tst.2021.9010026 · Cited by 186
Self-citationClustered federated learning with adaptive local differential privacy on heterogeneous IoT data
He, Z., Wang, L., & Cai, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299947 · Cited by 143
Metadata
- Authors
- Cai, Z., He, Z., Guan, X., & Li, Y.
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2016
- Pages
- 1-1
- ISSN
- 1545-5971 (ISSN-L)
- OpenAlex
- W2526910689
- Cited by
- 528
- References
- 37
- FWCI
- 42.33
- Type
- journal article
- Language
- en
- Open access
- closed
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