We develop privacy-preserving tools and research at
MIT that will help shape the future of data governance.
We develop privacy-preserving tools and research at MIT that will help shape the future of data governance.


The MIT Future of Data initiative leads technical research on privacy-enhancing data systems and analytic techniques, creates a policy dialogue with extensive public engagement, and develops new educational opportunities related to data governance technology and public policy.

Database Systems

Develop new data management architectures to provide enterprises with purpose management, provable delete and automated audit accountability tools for managing personal data according to legal rules and institutional commitments: GDPR, along with other current and proposed privacy laws, pose data governance challenges that cannot be met with existing database architectures. By combining the best database systems expertise with policy awareness, we will develop models for new systems that can help govern data at scale and enable engagement with policymakers about the most effective approaches.

Current projects

A System for Trusted, Universal, Multi-Institution Data Provenance (Michael Cafarella)

Applied Cryptography

Deepen the application of privacy-preserving cryptographic techniques to real-world policy challenges associated with handling personal data: By combining scientific insights into cryptography, we can build usable systems and associated policy frameworks for working with de-identified data.

The world’s leading privacy laws look to private computation and other cryptographically-powered data handling techniques to enable uses of personal data while limiting privacy risk. We will bring together the cryptographers and public policy experts to expand the technical options available and contribute to the public policy dialogue on this question.

Privacy-Preserving Machine Learning

Develop privacy preserving, trustworthy machine learning (ML)systems meeting global legal requirements and providing explanation and bias assessment: The sustainable and trustworthy growth of ML systems depends on both technical advances in how personal data is obtained and handled in the ML pipeline, as well as public policy dialogue to agree on norms that meet society’s expectations for both privacy and advancing human knowledge.

Data Portability And New Information Architectures​

Design new protocols for managing personal data flow across APIs to enable support for data portability requirements while maintaining usage limits and accountability: Data portability has clear benefits for competition and significant impact on privacy, perhaps positive. We will explore new technical approaches to greater individual control over data with an eye to the underlying privacy risks and benefits.

View Our Research