Name
Abstract | ||
|---|---|---|
Data anonymization is required before a big-data business can run effectively without compromising the privacy of personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1587/transfun.E101.A.19 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
data privacy, anonymization, re-identification risk, big data | Data science,Record linkage,Theoretical computer science,Information privacy,Big data,Mathematics | Journal |
Volume | Issue | ISSN |
E101A | 1 | 0916-8508 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| hiroaki kikuchi | 1 | 22 | 16.34 |
| Takayasu Yamaguchi | 2 | 2 | 1.49 |
| Koki Hamada | 3 | 61 | 10.92 |
| Yuji Yamaoka | 4 | 9 | 4.63 |
| Hidenobu Oguri | 5 | 0 | 0.34 |
| Jun Sakuma | 6 | 345 | 37.29 |