Name
Abstract | ||
|---|---|---|
In the age of big data, privacy and utility are of fundamental importance on statistical analysis. In this paper, we investigate the privacy-utility trade-off of k-subset mechanism which can be regarded as a generalization of randomized response mechanism. For a k-subset mechanism, the entropy of output random variable is described as privacy metric whereas the mutual information is considered as utility measurement. It is proved that with the increase of privacy budget, the entropy of output random variable monotonically decreases, but the mutual information increases, which illustrates the trade-off of privacy and utility. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NANA.2018.8648741 | 2018 International Conference on Networking and Network Applications (NaNA) |
Keywords | Field | DocType |
trade-off,k-subset mechanism,entropy,mutual information | Monotonic function,Random variable,Differential privacy,Computer science,Theoretical computer science,Trade-off,Mutual information,Randomized response,Big data,Distributed computing,Statistical analysis | Conference |
ISBN | Citations | PageRank |
978-1-5386-8304-0 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihui Zhou | 1 | 34 | 6.71 |
| Guangchen Song | 2 | 0 | 0.68 |
| Hai Liu | 3 | 495 | 46.73 |
| Laifeng Lu | 4 | 0 | 3.72 |