000 04007nam a22005775i 4500
001 978-1-4419-6569-1
003 DE-He213
005 20140220084510.0
007 cr nn 008mamaa
008 100528s2010 xxu| s |||| 0|eng d
020 _a9781441965691
_9978-1-4419-6569-1
024 7 _a10.1007/978-1-4419-6569-1
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aUMT
_2bicssc
072 7 _aCOM021000
_2bisacsh
082 0 4 _a005.74
_223
100 1 _aGkoulalas-Divanis, Aris.
_eauthor.
245 1 0 _aAssociation Rule Hiding for Data Mining
_h[electronic resource] /
_cby Aris Gkoulalas-Divanis, Vassilios S. Verykios.
264 1 _aBoston, MA :
_bSpringer US,
_c2010.
300 _aXX, 138p. 120 illus., 60 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Database Systems,
_x1386-2944 ;
_v41
505 0 _aFundamental Concepts -- Background -- Classes of Association Rule Hiding Methodologies -- Other Knowledge Hiding Methodologies -- Summary -- Heuristic Approaches -- Distortion Schemes -- Blocking Schemes -- Summary -- Border Based Approaches -- Border Revision for Knowledge Hiding -- BBA Algorithm -- Max-Min Algorithms -- Summary -- Exact Hiding Approaches -- Menon's Algorithm -- Inline Algorithm -- Two-Phase Iterative Algorithm -- Hybrid Algorithm -- Parallelization Framework for Exact Hiding -- Quantifying the Privacy of Exact Hiding Algorithms -- Summary -- Epilogue -- Conclusions -- Roadmap to Future Work.
520 _aPrivacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique on data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of “hiding” sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. Specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.
650 0 _aComputer science.
650 0 _aOperating systems (Computers).
650 0 _aData structures (Computer science).
650 0 _aComputer software.
650 0 _aDatabase management.
650 0 _aInformation systems.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Systems Applications (incl.Internet).
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aData Structures, Cryptology and Information Theory.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aPerformance and Reliability.
700 1 _aVerykios, Vassilios S.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441965684
830 0 _aAdvances in Database Systems,
_x1386-2944 ;
_v41
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-6569-1
912 _aZDB-2-SCS
999 _c110659
_d110659