Name
Abstract | ||
|---|---|---|
Private data sometimes must be made public. A corporation may keep its customer sales data secret, but reveals totals by sector for marketing reasons. A hospital keeps individual patient data secret, but might reveal outcome information about the treatment of particular illnesses over time to support epidemiological studies. In these and many other situations, aggregate data or partial data is revealed, but other data remains private. Moreover, the aggregate data may depend not only on private data but on public data as well, e.g. commodity prices, general health statistics. Our GhostDB platform allows queries that combine private and public data, produce aggregates to data warehouses for OLAP purposes, and reveal exactly what is desired, neither more nor less. We call this functionality "revelation on demand". |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10619-009-7035-x | Distributed and Parallel Databases |
Keywords | Field | DocType |
Confidentiality and privacy,Secure device,Data warehousing,Indexing model,Query processing,Aggregate computation | Data warehouse,Corporation,On demand,Data quality,Commodity,Computer science,Revelation,Aggregate data,Online analytical processing,Marketing,Database,Distributed computing | Journal |
Volume | Issue | ISSN |
25 | 1-2 | 0926-8782 |
Citations | PageRank | References |
2 | 0.38 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Anciaux | 1 | 148 | 22.93 |
| Mehdi Benzine | 2 | 23 | 2.63 |
| Luc Bouganim | 3 | 670 | 92.80 |
| Philippe Pucheral | 4 | 514 | 71.89 |
| Dennis Shasha | 5 | 6661 | 1466.04 |