You May Also Like... Privacy: Recommendation Systems Meet PIR

Authors: Adithya Vadapalli (Indiana University), Fattaneh Bayatbabolghani (University of California, Berkeley), Ryan Henry (University of Calgary)

Volume: 2021
Issue: 4
Pages: 30–53
DOI: https://doi.org/10.2478/popets-2021-0059

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Abstract: We describe the design, analysis, implementation, and evaluation of PIRSONA, a digital content delivery system that realizes collaborative-filtering recommendations atop private information retrieval (PIR). This combination of seemingly antithetical primitives makes possible—for the first time—the construction of practically efficient e-commerce and digital media delivery systems that can provide personalized content recommendations based on their users’ historical consumption patterns while simultaneously keeping said consumption patterns private. In designing PIRSONA, we have opted for the most performant primitives available (at the expense of rather strong non-collusion assumptions); namely, we use the recent computationally 1private PIR protocol of Hafiz and Henry (PETS 2019.4) together with a carefully optimized 4PC Boolean matrix factorization.

Keywords: Multiparty computation; distributed point functions; private information retrieval; recommendation systems

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