## Abstract

Given two or more parties possessing large, confidential datasets, we consider the problem of securely computing the k^{th}-ranked element of the union of the datasets, e.g. the median of the values in the datasets. We investigate protocols with sublinear computation and communication costs. In the two-party case, we show that the k^{th}-ranked element can be computed in log k rounds, where the computation and communication costs of each round are O(log M), where log M is the number of bits needed to describe each element of the input data. The protocol can be made secure against a malicious adversary, and can hide the sizes of the original datasets. In the multi-party setting, we show that the k^{th}-ranked element can be computed in log M rounds, with O(s log M) overhead per round, where s is the number of parties. The multi-party protocol can be used in the two-party case and can also be made secure against a malicious adversary.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Editors | Christian Cachin, Jan Camenisch |

Publisher | Springer Verlag |

Pages | 40-55 |

Number of pages | 16 |

ISBN (Print) | 3540219358, 9783540219354 |

DOIs | |

State | Published - 2004 |

Externally published | Yes |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 3027 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

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