Google Online Security Blog: Helping organizations do more without collecting more data

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Helping organizations do more without collecting more data

19 de junio de 2019

Posted by Amanda Walker, Engineering Director; Sarvar Patel, Software Engineer; and Moti Yung, Research Scientist, Private Computinginvest in new researchPassword Checkupprivate set intersection (PSI)open-source availabilitysecure multi-party computation Collaborating with data in privacy-safe ways A deeper look at the technology

Private set intersection allows two parties to privately join their sets and discover the identifiers they have in common. We use an oblivious variant which only marks encrypted identifiers without learning any of the identifiers.

Homomorphic encryption allows certain types of computation to be performed directly on encrypted data without having to decrypt it first, which preserves the privacy of raw data. Throughout the process, individual identifiers and values remain concealed. For example, you can count how many identifiers are in the common set or compute the sum of values associated with marked encrypted identifiers – without learning anything about individuals.

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Using multi-party computation to solve real-world problemsmaking privacy technology more widely availablecollaborative machine learning

Public policy - if a government implements new wellness initiatives in public schools (e.g. better lunch options and physical education curriculums), what are the long-term health outcomes for impacted students?
Diversity and inclusion - when industries create new programs to close gender and racial pay gaps, how does this impact compensation across companies by demographic?
Healthcare - when a new preventative drug is prescribed to patients across the country, does it reduce the incidence of disease?
Car safety standards - when auto manufacturers add more advanced safety features to vehicles, does it coincide with a decrease in reported car accidents?

Private Join and Compute keeps individual information safe while allowing organizations to accurately compute and draw useful insights from aggregate statistics. By sharing the technology more widely, we hope this expands the use cases for secure computing. To learn more about the research and methodology behind Private Join and Compute, read the full paper and access the open source code and documentation. We’re excited to see how other organizations will advance MPC and cryptography to answer important questions while upholding individual privacy.

Acknowledgements

Product Manager - Nirdhar Khazanie
Software Engineers - Mihaela Ion, Benjamin Kreuter, Erhan Nergiz, Quan Nguyen, and Karn Seth
Research Scientist - Mariana Raykova