Name
Abstract | ||
|---|---|---|
The omnipresence of services offered by diverse applications leads customers to share more and more personal data, among which some are sensitive. Dishonest entities perform inference attacks by querying non-sensitive data in order to deduce the stored sensitive data. Detecting those attacks is still an open problem in a setting where a dishonest entity has access to distinct data controllers' databases containing data collected from the same customer. This problem has been addressed considering a centralized detection system. However, this approach is limited because of this centralized nature where the system protects the customers' privacy at the expense of the data controllers' privacy. Hence, we propose in this article the description of a distributed architecture to detect inference attacks in a multi-database context, while preserving the privacy of both the applications and the customers. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/COMPSAC54236.2022.00246 | 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022) |
Keywords | DocType | Citations |
distributed inference detection system, multi-database context, data privacy | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sad Rafik | 1 | 0 | 0.34 |
| Paul Lachat | 2 | 0 | 0.34 |
| Nadia Bennani | 3 | 56 | 13.91 |
| Veronika Rehn-Sonigo | 4 | 0 | 0.34 |