Name
Abstract | ||
|---|---|---|
Currently, all the existing studies with good privacy guarantees focus on a single privacy level. Namely, a certain degree of privacy protection is implemented on all anonymized data released. However, this is not consistent with the actual scene that the different roles have different levels of privacy. From this point of view, this paper proposed a scenario with multi-user and multi-granularity privacy protection, and proposed the l-increment privacy protection model. On this basis, we put forward a generalization algorithm, which can meet the requirement for multi-user and multi-granularity, and reduce greatly the amount of information loss resulting from data generalization for implementing data anonymization in the meanwhile. Our findings are verified by experiments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICDMW.2013.31 | ICDM Workshops |
Keywords | Field | DocType |
privacy preservation,different role,different level,anonymized data,single privacy level,data anonymization,privacy protection,good privacy guarantee,data generalization,multi-granularity privacy protection,l-increment privacy protection model,data privacy | Data mining,Information loss,Computer science,Computer security,Data anonymization,Privacy Level,Granularity,Information privacy,Privacy software,Multi-user | Conference |
ISSN | Citations | PageRank |
2375-9232 | 0 | 0.34 |
References | Authors | |
24 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Li | 1 | 475 | 67.20 |
| Xiangmang He | 2 | 0 | 0.34 |
| HuaHui Chen | 3 | 17 | 5.75 |
| Yihong Dong | 4 | 30 | 5.70 |
| Yefang Chen | 5 | 1 | 1.37 |