Name
Abstract | ||
|---|---|---|
In this paper we illustrate a privacy framework named Indistinguishabley Privacy. Indistinguishable privacy could be deemed as the formalization of the existing privacy definitions in privacy preserving data publishing as well as secure multi-party computation. We introduce three representative privacy notions in the literature, Bayes-optimal privacy for privacy preserving data publishing, differential privacy for statistical data release, and privacy w.r.t. semi-honest behavior in the secure multi-party computation setting, and prove they are equivalent. To the best of our knowledge, this is the first work that illustrates the relationships of these privacy definitions and unifies them through one framework. |
Year | Venue | Keywords |
|---|---|---|
2013 | Transactions on Data Privacy | semantic security,representative privacy notion,privacy definitions revisited,bayes-optimal privacy,indistinguishable privacy,privacy w,data publishing,privacy definition,privacy framework,existing privacy definition,statistical data release,differential privacy |
Field | DocType | Volume |
Data mining,Internet privacy,Semantic security,Differential privacy,Privacy by Design,Computer science,Computer security,Personally identifiable information,Data publishing,Information privacy,Privacy software | Journal | 6 |
Issue | ISSN | Citations |
3 | 1888-5063 | 1 |
PageRank | References | Authors |
0.36 | 28 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinfei Liu | 1 | 91 | 11.12 |
| Li Xiong | 2 | 2335 | 142.42 |
| Jun Luo | 3 | 222 | 26.61 |