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

  • Jens KöhlerEmail author
  • Hannes Hartenstein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8872)


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 \(\ell \)-diversity and can be used to outsource databases. However, indexes on anonymized data significantly differ from plaintext indexes both in terms of usage and possible performance gains. In most cases, 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 \(\ell \)-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.


Database-as-a-service Anonymized indexes \(\ell \)-diversity Performance optimization 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Karlsruhe Institute of Technology (KIT), Steinbuch Centre for Computing (SCC) and Institute of TelematicsKarlsruheGermany

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