Can we speak privately?

Unfortunately, our current options for private communication are limited, hamstrung by their reliance on a single-trusted-origin data publication model, high latencies, and security vulnerabilities.

We think it is possible to design a scalable system that doesn’t sacrifice latency for privacy.

We know that there has been a lot of exciting work improving security/privacy tradeoffs that has not yet been translated into working technology, and we believe that there is great potential for further progress.

RFP-014: Private retrieval of data solicits proposals to explore and develop viable mechanisms for reader-private communications: we intend to fund both research leading to deployment-ready design sketches for private communication mechanisms as well as development activities implementing systems prototypes.

We are interested in funding projects which: 

• Explore new mechanisms for private communication (e.g. with cryptographic, information theoretic, or statistical bases)
• Relax the traditional ‘web’ assumptions of a single origin to engage with the possibilities of pre-distributed CDN or content-addressed data.
• Prototype the use of novel network-layer privacy technologies in real systems.

Accepted proposals will receive up to $300,000 in funding. All work must be released under an open-source license and may find usage in other systems. The first phase of this call will close on 1 March 2023 or earlier if awarded.

We encourage interested researchers to reach out to us by visiting #private-retrieval in the Lodestar discord For formal questions, please email research-grants@protocol.ai. Submit your proposal using our application management system at grants.protocol.ai.