Google Releases Open Source Tool For Computational Privacy

google private join and compute

Google’s new multi-party computation tool allows companies to work together with confidential data sets.

Google is releasing a new open-source cryptographic tool aimed at boosting privacy around sensitive data sets. The tool, called Private Join and Compute, is designed to help companies who are working together with confidential data sets.

Private Join and Compute, allows companies to share data in overlapping databases, which will stay encrypted during various calculations. They can then view the decrypted end result – whether it’s a count, sum or average – in the form of aggregated statistics.

“Using this cryptographic protocol, two parties can encrypt their identifiers and associated data, and then join them,” said Google in a Wednesday announcement. “They can then do certain types of calculations on the overlapping set of data to draw useful information from both datasets in aggregate. All inputs (identifiers and their associated data) remain fully encrypted and unreadable throughout the process.”

The technology’s aim is to keep data secure while enabling organizations to accurately compute and draw insights.

For instance, a government looking to implement new wellness initiatives may need to collect data from various schools regarding students’ personal health. This technology would allow the school to offer up the data so that it stays encrypted, without revealing the private information of students to the government, and ultimately the government could collect the secure aggregated statistics.

Private Join and Compute relies on a cryptographic protocol called private set intersection (PSI), which has previously been used in Google technology. It checks if user’s credentials were compromised by matching login credentials against an encrypted database of over 4 billion “safe” credentials. The protocol ultimately allows two parties to privately join their sets and discover the identifiers they have in common.

Then, during calculations, data remains concealed using a process called homomorphic encryption, which enables certain types of computation to be performed directly on encrypted data without having to decrypt it first.

“This combination of techniques ensures that nothing but the size of the joined set and the statistics (e.g. sum) of its associated values is revealed,” said Google. “Individual items are strongly encrypted with random keys throughout and are not available in raw form to the other party or anyone else.”

The concept of a secure computational process for data sets being shared between multiple parties is tempting: Especially with the number of breaches, misconfigured datasets and other accidents revolving around third-party handling of data.

However, challenges remain: The concept of a cryptographic-secure computation protocols has long been the subject of research, but it has faced roadblocks for widespread adoption beyond academia. Those challenges have included finding effective ways to tailor encryption tools and tactics to actual real-life applications.

“Private Join and Compute keeps individual information safe while allowing organizations to accurately compute and draw useful insights from aggregate statistics,” said Google. “By sharing the technology more widely, we hope this expands the use cases for secure computing.”

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