Protecting Privacy through Homomorphic Encryption
Private movie recommendations for children
Publication
, Chapter
Pham, A; Samragh, M; Wagh, S; Wenger, E
January 4, 2022
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.
Duke Scholars
Citation
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Pham, A., Samragh, M., Wagh, S., & Wenger, E. (2022). Private movie recommendations for children. In Protecting Privacy through Homomorphic Encryption (pp. 163–167). https://doi.org/10.1007/978-3-030-77287-1_13
Pham, A., M. Samragh, S. Wagh, and E. Wenger. “Private movie recommendations for children.” In Protecting Privacy through Homomorphic Encryption, 163–67, 2022. https://doi.org/10.1007/978-3-030-77287-1_13.
Pham A, Samragh M, Wagh S, Wenger E. Private movie recommendations for children. In: Protecting Privacy through Homomorphic Encryption. 2022. p. 163–7.
Pham, A., et al. “Private movie recommendations for children.” Protecting Privacy through Homomorphic Encryption, 2022, pp. 163–67. Scopus, doi:10.1007/978-3-030-77287-1_13.
Pham A, Samragh M, Wagh S, Wenger E. Private movie recommendations for children. Protecting Privacy through Homomorphic Encryption. 2022. p. 163–167.