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Private Set Intersection and Compute

In: Protecting Privacy through Homomorphic Encryption

Author

Listed:
  • Flavio Bergamaschi

    (SRI International)

  • Tancrède Lepoint

    (SRI International)

  • Peter Leihn

    (SRI International)

  • Sreekanth Kannepalli

    (Microsoft)

Abstract

We consider the scenario where two or more data owners would like to join their data and compute some functions over the intersection of their data in a privacy-preserving way and without disclosing their dataset to each other nor the intersection of their datasets. This scenario is primarily motivated by the following aspects:

Suggested Citation

  • Flavio Bergamaschi & Tancrède Lepoint & Peter Leihn & Sreekanth Kannepalli, 2021. "Private Set Intersection and Compute," Springer Books, in: Kristin Lauter & Wei Dai & Kim Laine (ed.), Protecting Privacy through Homomorphic Encryption, pages 97-104, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-77287-1_6
    DOI: 10.1007/978-3-030-77287-1_6
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