Abstract
Data sharing between multiple parties, e.g., advertisers and publishers, may be restricted owing to technical limitations as well as compliance with regulation related to data privacy. This can limit computation of certain parameters, e.g., the effectiveness of an online advertisement as published by a publisher. This disclosure describes mechanisms to enable privacy-compliant measurement without data sharing between multiple parties. The described techniques enable a publisher and an advertiser to use secure multi-party Computation (MPC) to compute an aggregate result on two joint datasets without either party revealing their private inputs. The described techniques can be implemented using virtual private cloud infrastructure respectively controlled by each party and configured to enable communication between the MPC programs executed by each party.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Secure Multiparty Computation For Private Measurement Of Advertising Lift", Technical Disclosure Commons, (October 12, 2020)
https://www.tdcommons.org/dpubs_series/3665