| Project/Area Number |
24650064
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
SATO Issei 東京大学, 情報基盤センター, 助教 (90610155)
|
|---|
| Project Period (FY) |
2012-04-01 – 2014-03-31
|
| Project Status |
Completed (Fiscal Year 2013)
|
| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2013: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2012: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
|---|
| Keywords | プライバシー保護 / データマイニング / 差分プライバシー / ビッグデータ / 個人情報保護 / データベース / 匿名化 / 相関 / 個人情報 / 機械学習 / 雑音 |
|---|
| Research Abstract |
The differential privacy is the technology of adding noise to an answer for the given query. The differential privacy for data base with correlated data records, however, has not get much attention. In this research, we find the counter intuitive phenomena that an adversary with small amount of background knowledge about correlations can get more privacy information than the adversary with big amount of these knowledge. Then, we build the Bayesian privacy model which explains this kind of phenomena and improve this situation. We also show the approximation algorithm that gives us the proper parameter value of Laplace distribution employed in differential privacy which make the provability of information leakage less than previously determined threshold.
In addition, we investigated the problems which are caused by added noise in differential privacy.
|
