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Originalveröffentlichung
DOI: 10.1007/978-3-319-17016-9_8

Index Optimization for L-Diversified Database-as-a-Service

Köhler, J.; Hartenstein, H.

Abstract:
Preserving the anonymity of individuals by technical means when outsourcing databases to semi-trusted providers gained importance in recent years. Anonymization approaches exist that fulfill anonymity notions like l-diversity and can be used to outsource databases. However, using indexes on anonymized data to increase query execution performance significantly differs from using plaintext indexes and it is not clear whether using an anonymized index is beneficial or not. In this paper, we present Dividat, an approach that makes anonymized database outsourcing more practical and deployable by optimizing the indexing of -diversified data. We show that the efficiency of anonymized indexes differs from traditional indexes and performance gains of a factor of 5 are possible by optimizing indexing strategies. We propose strategies to determine which indexes should be created for a given query workload and used for a given query. To apply these strategies without actually creating each possible index, we propose and validate models that estimate the performance of anonymized index tables a-priori.


Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Steinbuch Centre for Computing (SCC)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Jahr 2015
Sprache Englisch
Identifikator ISBN: 978-331917015-2
ISSN: 0302-9743
KITopen ID: 1000043776
HGF-Programm 46.12.03; LK 01
Erschienen in 9th International Workshop on Data Privacy Management, DPM 2014, 7th International Workshop on Autonomous and Spontaneous Security, SETOP 2014 and 3rd International Workshop on Quantitative Aspects in Security Assurance, QASA 2014 held in conjunction with 19th Annual European Research Event in Computer Security Symposium, ESORICS 2014; Wroclaw; Poland; 10 September 2014 through 11 September 2014. Ed.: J. Posegga
Verlag Springer, Cham
Seiten 114-132
Serie Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; 8872
Projektinformation KASTEL I (BMBF, 01BY1172 / 16BY1172)
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