000			03479nam a22005535i 4500
001			978-0-387-46274-5
003			DE-He213
005			20161121230609.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387462745 _9978-0-387-46274-5
024	7		_a10.1007/978-0-387-46274-5 _2doi
050		4	_aQA76.9.D35
072		7	_aUMB _2bicssc
072		7	_aURY _2bicssc
072		7	_aCOM031000 _2bisacsh
082	0	4	_a005.74 _223
100	1		_aWang, Lingyu. _eauthor.
245	1	0	_aPreserving Privacy in On-Line Analytical Processing (OLAP) _h[electronic resource] / _cby Lingyu Wang, Sushil Jajodia, Duminda Wijesekera.
264		1	_aBoston, MA : _bSpringer US, _c2007.
300			_aXII, 180 p. 20 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Information Security, _x1568-2633 ; _v29
505	0		_aOLAP and Data Cubes -- Inference Control in Statistical Databases -- Inferences in Data Cubes -- Cardinality-based Inference Control -- Parity-based Inference Control for Range Queries -- Lattice-based Inference Control in Data Cubes -- Query-driven Inference Control in Data Cubes -- Conclusion and Future Direction.
520			_aOn-Line Analytic Processing (OLAP) systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. Existing inference control methods in statistical databases usually exhibit high performance overhead and limited effectiveness when applied to OLAP systems. Preserving Privacy in On-Line Analytical Processing reviews a series of methods that can precisely answer data cube-style OLAP queries regarding sensitive data while provably preventing adversaries from inferring the data. How to keep the performance overhead of these security methods at a reasonable level is also addressed. Achieving a balance between security, availability, and performance is shown to be feasible in OLAP systems. Preserving Privacy in On-Line Analytical Processing is designed for the professional market, composed of practitioners and researchers in industry. This book is also appropriate for graduate-level students in computer science and engineering. .
650		0	_aComputer science.
650		0	_aComputer communication systems.
650		0	_aData structures (Computer science).
650		0	_aData encryption (Computer science).
650		0	_aDatabase management.
650	1	4	_aComputer Science.
650	2	4	_aData Structures, Cryptology and Information Theory.
650	2	4	_aData Encryption.
650	2	4	_aDatabase Management.
650	2	4	_aInformation Systems Applications (incl. Internet).
650	2	4	_aComputer Communication Networks.
700	1		_aJajodia, Sushil. _eauthor.
700	1		_aWijesekera, Duminda. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387462738
830		0	_aAdvances in Information Security, _x1568-2633 ; _v29
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-46274-5
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c501116 _d501116