Name
Abstract | ||
|---|---|---|
Pk-anonymization is a data anonymization method that employs randomization. Pk-anonymization guarantees Pk-anonymity, which is an extension of probabilistic k-anonymity. To implement this method, we assign random noise to records to reduce the probability of identifying record owners to less than 1/k. Existing methods assign noise using a Laplace distribution, and determine the variance of the Laplace distribution at a desired value of k to satisfy Pk-anonymity. In this paper, we propose an algorithm that improves the implementation of Pk-anonymization with smaller variance. We demonstrate the advantage of the proposed method by comparing it with an existing method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SRDSW.2014.28 | Reliable Distributed Systems Workshops |
Keywords | Field | DocType |
data privacy,probability,random noise,Laplace distribution,Pk-anonymity,Pk-anonymization,data anonymization method,privacy measure,probabilistic k-anonymity,probability,random noise,randomization,anonymization,database,preserving privacy | Data mining,Laplace distribution,Computer science,Random noise,Data anonymization,Correlation,Probabilistic logic,Information privacy | Conference |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miho Kakizawa | 1 | 1 | 0.36 |
| Chiemi Watanabe | 2 | 133 | 23.21 |
| Furukawa, R. | 3 | 58 | 5.21 |
| Tsubasa Takahashi | 4 | 1 | 1.03 |