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Differentially Private Frequent Sequence Mining via Sampling-based Candidate Pruning
In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, i...
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| Publicado no: | Proc Int Conf Data Eng |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2015
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4788512/ https://ncbi.nlm.nih.gov/pubmed/26973430 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ICDE.2015.7113354 |
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