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Name: Chen Yuan
Type: User
Name: Chen Yuan
Type: User
Private computation framework library allows developers to perform randomized controlled trials, without leaking information about who participated or what action an individual took. It uses secure multiparty computation to guarantee this privacy. It is suitable for conducting A/B testing, or measuring advertising lift and learning the aggregate statistics without sharing information on the individual level.
FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data. Both parties have dedicated cloud computing instances living on separate Virtual Private Clouds (VPCs) that are connected to allow network communication. The FBPMP products that have been implemented are Private Lift and Private Attribution. It’s expected that more products will be implemented and added to the Private Measurement suite.
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