2009
DOI: 10.1007/s10619-009-7035-x
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Revelation on demand

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 6 Distrib Parallel Databases (2009) 25: 5-28 gregates to data warehouses for OLAP purposes, and reveal exactly what is desired, neither more nor less. We call this functionality "rev… Show more

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Cited by 3 publications
(7 citation statements)
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“…The parameters for security evaluation are shown in Table 4. they are separated into system and user revelation respectively [30]. There are certain ways in which the user of a system might reveal his/her authentication key.…”
Section: Rq3mentioning
confidence: 99%
“…The parameters for security evaluation are shown in Table 4. they are separated into system and user revelation respectively [30]. There are certain ways in which the user of a system might reveal his/her authentication key.…”
Section: Rq3mentioning
confidence: 99%
“…However, with scarce RAM, FTL cannot hide NAND Flash constraints without large performance degradation. 5 Typically, random writes are two or three orders of magnitude more costly than sequential writes on SD cards. 6 In addition, FTL are black box firmwares with behaviors difficult to predict and optimize.…”
Section: Massive Indexing Schemesmentioning
confidence: 99%
“…Let us first consider Select/Project/Join queries (SPJ). The very small ratio between RAM and database size leads to use generalized selection and join indexes [5,28,32,34]. These indexes are called generalized in the sense that they capture the transitive relationships which may exist between tuples of different tables.…”
Section: Which Indexes?mentioning
confidence: 99%
See 1 more Smart Citation
“…Peer-to-peer applications use Bloom filters to represent peer contents, to enable query routing in unstructured P2P networks [9][10][11][12], and for estimating item novelty [13]. In addition, Bloom filters are used for optimizing collaboration protocols, such as collaborative caching [14] and content reconciliation [15], as well as for query optimization in databases with confidential data, to enable join execution without revealing information [16]. In general, the compactness of Bloom filter representations and the constant cost for membership tests is appealing for a wide range of data-intensive distributed systems.…”
mentioning
confidence: 99%