The major code projects of the Trust::Data Consortium are:

  • OPAL

  • Digital Identity and Privacy

  • Blockchain Technology

  • MIT Enigma

  • OpenPDS



The OPAL project embraces three key concepts with the goal of making a broad array of data available for inspection and analysis without violating personal data privacy:

  1. Move the algorithm to the data. Performing algorithm-execution on data at the location of the data repository means that raw data never leaves its repository, and access to it is controlled by the repository owner. Only aggregate answers or "Safe Answers" are returned.

  2. Algorithms must be open. Algorithms must be openly published, studied and vetted by experts to be “safe” from violating privacy requirements and other needs stemming from the context of their use.

  3. Data is always in an encrypted state. Data must be in an encrypted state while being transmitted and during computation.

Through public-private partnerships, OPAL pilots are underway to assess the feasibility and value of statistical indicators derived through data analysis using the OPAL platform.

  • Open Algorithms (OPAL) principles paper (PDF)

  • A. Pentland, D. Shrier, T. Hardjono, and I. Wladawsky-Berger, “Towards an Internet of Trusted Data: Input to the Whitehouse Commis- sion on Enhancing National Cybersecurity,” in Trust::Data - A New Framework for Identity and Data Sharing, T. Hardjono, A. Pentland, and D. Shrier, Eds. Visionary Future, 2016, pp. 21–49.

  • T. Nishikata, T. Hardjono and A. Pentland, Social Capital Accounting, October 2018.



The identity problem today is a data-sharing problem. Today the fixed attributes approach adopted by the consumer identity management industry provides only limited information about an individual, and therefore is of limited value to the service providers and other participants in the identity ecosystem. This project investigates the use of the Open Algorithms (OPAL)  to obtain better insight about an individual's digital persona in a given context through a collective sharing of algorithms, governed through a trust network. Algorithms for specific data-sets must be vetted to be privacy-preserving, fair and free from bias.  

The project recognizes that a new model for privacy-preserving identities is needed if blockchain systems are to operate at a global scale: it must allow entities in the ecosystem to (i) verify the “quality” or security of an identity, and (ii) to assess the relative “freedom” or independence of an identity from any given authority (e.g. government, businesses, etc.), and (iii) to assess the source of trust for a digital identity



Our lab at the Massachusetts Institute of Technology is working on creating a digital currency suitable for large-scale transactional purposes. Called Tradecoin, it will be indelibly logged on a blockchain and anchored at all times to a basket of real-world assets such as crops, energy or minerals. Doing so will help stabilize its value and make it easier for the public to trust it. The core idea is that a broadly useful currency needs both human trust and efficient trade systems.

A digital Tradecoin built on a distributed ledger can allow alliances of small nations, businesses, commercial traders, credit unions or even farmers to put together enough assets to back a large, liquid currency that would potentially be as trustworthy and at least as efficient as the national currencies used by the World Bank and the International Monetary Fund. By design, the principles behind currencies such as Tradecoin are fundamentally different from cryptocurrencies like Bitcoin, which are not backed by real-world assets and do not involve alliances.



The world needs data in order to operate. This includes governments, enterprises, local municipalities and communities. AI, machine learning and analytics will be important tools for society to function. These tools need data. However, there are some big open questions about personal data and privacy. There is also the question about the ownership and access to data. But how do we actually empower consumers? How do individuals gain easy access to their personal data? How do we include consumers in the personal data ecosystem and remunerate them?

The first part is to enable individuals to get copies of their data. The idea is to use a software agent within the operating system of our device that collects copies of our data. The software agent copies all the data traffic coming into & out of my device. It then stores this copy of data into my personal data store, which can be on the device, on my server or in my cloud.

Secondly, to protect privacy you must send the algorithm to the data. Data must never leave the data repository. Only insights or answers are generated, and returned. This is what we call the MIT Open Algorithms Principles.

Thirdly, personal data becomes valuable when groups of people get together and pool their personal data. When a group of people pool their resources, they have more bargaining power. They can require a fiduciary agreement with anyone who wants to access their data.

Organizations such as credit unions and trade unions maybe be suitable for hosting personal data stores of their members, and running local analytics in order to derive insights about the need of their members. These insights can then be used by the organization to act for the positive benefit of their members (e.g. negotiating better rates for external services, group discount purchasing, personalized health). By pooling their personal data, the group can decide on the remuneration model for the members of the group. By using the OPAL principles, the privacy of the group members can be protected. By including fiduciary obligations in the data access agreement, they prevent the re-selling of personal data into the shadow economy.


We are developing an open source platform that supports the Enigma design.  In particular, it focuses on the use of Secure Multi-Party Computation (S-MPC) over both plaintext data and data that has been "split" into shares (e.g. using a Linear Secret Sharing Scheme).  The Enigma design allows the underlying the P2P nodes (e.g. in a blockchain) to store the shares (as off-chain storage), and allows for the reconstruction of the origin data through a minimal (threshold) number of shares. In combination with OPAL, the design provides a way to increase the resilience of backend data repositories.



MIT OpenPDS is platform for personal data interchange across multiple data-repositories. Today the typical end-user generates a large number of data as the by product of living in the digital space.  These multiple data-repositories represent valuable data-sets capturing an individual's life. OpenPDS provides a platform for individuals and organizations to manage these disparate repositories by providing a uniform user interface.

More importantly, for queriers seeking to access data within an repository OpenPDS filters response through a Safe Answers engine, and provides the data-owner a tunable degree of privacy-preservation. OpenPDS build on OpenPDS (v1) by adding features, such as a simple multi-party computation capability, simplified "smart contracts", integration into the OpenID-Connect server for authentication and authorization and UMA1.0 for consent management.