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Private Movie Recommendations for Children

In: Protecting Privacy through Homomorphic Encryption

Author

Listed:
  • Anh Pham

    (UC San Diego, Department of Biomedical Informatics)

  • Mohammad Samragh

    (UC San Diego, Electrical and Computer Engineering)

  • Sameer Wagh

    (UC Berkeley, RISE Lab)

  • Emily Wenger

    (University of Chicago, Computer Science)

Abstract

Data-driven business models such as recommender systems (Netflix, Pandora) and targeted advertising platforms (Facebook, Google) heavily rely on consumer data and information about individual behavior patterns and preferences. In this work, we look at using Homomorphic Encryption as a tool to enable a privacy conscious recommender system that simultaneously allows the data-driven businesses while providing user privacy. We look at YouTube Kids as a target application.

Suggested Citation

  • Anh Pham & Mohammad Samragh & Sameer Wagh & Emily Wenger, 2021. "Private Movie Recommendations for Children," Springer Books, in: Kristin Lauter & Wei Dai & Kim Laine (ed.), Protecting Privacy through Homomorphic Encryption, pages 163-167, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-77287-1_13
    DOI: 10.1007/978-3-030-77287-1_13
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