Name
Abstract | ||
|---|---|---|
The de-anonymization attack using personalized transition matrices is known as one of the most successful approaches to link anonymized traces with users. However, since many users disclose only a small amount of location information to the public in their daily lives, the amount of training data available to the adversary can be very small. The aim of this paper is to quantify the risk of de-anonymization in this realistic situation. To achieve this aim, we utilize the fact that spatial data can form group structure, and propose group sparsity tensor factorization to train the personalized transition matrices that capture spatial group structure from a small amount of training data. We apply our training method to the de-anonymization attack, and evaluate it using the Geolife dataset. The results show that the training method using tensor factorization outperforms the Maximum Likelihood estimation method, and is further improved by incorporating group sparsity regularization. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/Trustcom-BigDataSe-ISPA.2015.427 | TrustCom/BigDataSE/ISPA |
Field | DocType | Citations |
Spatial analysis,Data mining,Group structure,De-anonymization,Matrix (mathematics),Computer science,Markov chain,Maximum likelihood,Regularization (mathematics),Artificial intelligence,Tensor factorization,Machine learning | Conference | 4 |
PageRank | References | Authors |
0.38 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takao Murakami | 1 | 55 | 15.02 |
| Atsunori Kanemura | 2 | 75 | 12.78 |
| Hideitsu Hino | 3 | 99 | 25.73 |