Name
Abstract | ||
|---|---|---|
Sharing data is gaining importance in recent years due to proliferation of social media and a growing tendency of governments to gain citizens' trust through being transparent. Data dissemination, however, increases chance of compromising privacy sensitive data, which undermines trust of data subjects (e.g., users and citizens). Data disseminators are morally, ethically, and legally responsible for any misuse of the disseminated data. Therefore, privacy enhancement techniques are often used to prevent unsavory disclosure of personal data. Data recipients, nevertheless, are sometimes able to derive (part of) privacy sensitive information by, for example, fusing the shared data with other data. This can be considered as a sort of data misuse. In this contribution, we investigate how having a feedback from data recipients to data disseminators is instrumental for detecting such data misuses (i.e., privacy breaches). We also elaborate on using feedback for defining and deriving context-dependent privacy-preferences of data disseminators. In this case, feedback acts as a means of privacy prevention. We provide a categorization of existing feedback based solutions and, in addition, describe our implementation of a feedback-based data dissemination solution in an eGovernment setting. Finally, we elaborate on the importance of real-time partial feedback mechanisms, as a rising and promising solution direction for preserving privacy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2691195.2691279 | ICEGOV |
Keywords | Field | DocType |
legal aspects,design,security,human factors,trust,privacy,data fusion,feedback,ethics,management,reliability,egovernment,economics,regulation,data sharing | Categorization,Internet privacy,Social media,Privacy by Design,Computer science,Computer security,Data sharing,Privacy policy,Dissemination,Information sensitivity,Information privacy | Conference |
Citations | PageRank | References |
4 | 0.44 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mortaza S. Bargh | 1 | 199 | 21.12 |
| Ronald Meijer | 2 | 56 | 5.86 |
| Sunil Choenni | 3 | 309 | 111.82 |
| Peter Conradie | 4 | 85 | 6.93 |