Name
Abstract | ||
|---|---|---|
PPDP (Privacy-Preserving Data Publishing) is technology that discloses personal information while protecting individual privacy. k-anonymity is a privacy model that should be achieved in PPDP. However, k-anonymity does not guarantee privacy against adversaries who have knowledge of even a few uncommon individuals in a population. In this paper, we propose a new model, called k-presence-secrecy, that prevents such adversaries from inferring whether an arbitrary individual is included in a personal data table. We also propose an algorithm that satisfies the model. k-presence-secrecy is a practical model because an algorithm that satisfies it requires only a PPDP target table as personal information, whereas previous models require a PPDP target table and almost all the background knowledge of adversaries. Our experiments show that, whereas an algorithm satisfying only k-anonymity cannot protect privacy, even against adversaries who have knowledge for one uncommon individual in a population, our algorithm can do so with less information loss and shorter execution time. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1587/transinf.2016DAP0015 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
privacy-preserving data publishing, k-anonymity | Internet privacy,Computer security,Computer science,Secrecy,k-anonymity,Privacy model,Privacy software | Journal |
Volume | Issue | ISSN |
E100D | 4 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuji Yamaoka | 1 | 9 | 4.63 |
| Kouichi Itoh | 2 | 285 | 22.56 |