Nov 22, 2016 · Re-identification attacks based on a Markov chain model have been widely studied to understand how anonymized traces are linked to users.

Abstract—Re-identification attacks based on a Markov chain model have been widely studied to understand how anonymized traces are linked to users.

This paper utilizes the fact that spatial data can form a group structure, and proposes group sparsity tensor factorization to effectively train the ...

This approach is known to enable users to be re-identified with high accuracy when an adversary trains a personalized transition matrix for each target user ...

Bibliographic details on Group Sparsity Tensor Factorization for Re-Identification of Open Mobility Traces.

Specifically, we considered an adversary who identifies, for each synthetic trace, an input user, whose training trace is used to synthesize the trace, from |U| ...

Group Sparsity Tensor Factorization for Re-Identification of Open Mobility Traces. 研究大学強化促進事業について. The program for promoting the enhancement ...

A succinct model for re-identification that outperforms the state-of-the-art Markov chain model and models a probability of being located in each region via ...

Group sparsity tensor factorization for de-anonymization of mobility traces. ... Group sparsity tensor factorization for re-identification of open mobility traces ...

Group Sparsity Tensor Factorization for Re-Identification of Open Mobility Traces. DOI PDF 被引用文献2件 参考文献40件 オープンアクセス. Takao Murakami.