We at Enigma are committed to building real solutions and making decentralization work for everyone — individuals as well as institutions. To help us achieve our ambition, we are proud to announce Enigma’s membership in the Enterprise Ethereum Alliance as well as the Decentralized Identity Foundation.

Enigma is a blockchain-based protocol that uses groundbreaking privacy technologies to enable scalable, end-to-end decentralized applications. With Enigma, “smart contracts” become “secret contracts,” where input data is kept hidden from nodes in the Enigma network that execute code. Without this functionality to protect sensitive data, blockchains and smart contracts are either useless or not truly decentralized. We believe Enigma is the missing piece to realizing a decentralized future.

Over the past months, we have been busy working with multiple major multinational companies and other organizations to explain and explore some of the benefits of the Enigma protocol. Enigma enables secure data processing and information sharing that enables partners to:

a) explore new avenues that were not possible either due to competition or regulation, and

b) reduce their liability on sensitive data.

We believe data is the most valuable asset in today’s world. The world’s largest companies are built around collecting, securing and building a competitive advantage around private data. While being a source of competitive edge, data is also heavily regulated as it covers the most private information about individuals. Thus, either due to conflicting business incentives or regulatory limitations, collaboration on data is currently not possible. Furthermore, data leakages and unintended use of our data are becoming real threats to our social fabric.

Enabling entities to securely share and collaborate on data will create tremendous long-term value for our society and our businesses.

Enigma’s privacy protocol enables organizations in at least three major areas:

1) Data matching / data joining

Most organizations have overlapping customer lists. Being able to join datasets based on a common denominator and run analysis on the shared data sets, without revealing any personal information, is critical. Today this problem is addressed by hiring and trusting a third party data analysis company. In most cases, the process involves a significant amount of paperwork to cover the company that owns the data from any liabilities. This third party is not only costly, but they also need to work with sensitive plaintext data. This creates the liability problem we mentioned above. Being able to match or join data sets without revealing sensitive information not only creates operational efficiency and limits liability, but also increases GDPR compliance with Article 6.4.e, which calls for encryption or pseudonymisation of user data. Furthermore, the Enigma protocol eliminates concerns about losing the competitive advantage derived from use of private data.

2) Proving identity or using Personally Identifiable Information (PII)

PII is very sensitive by nature. In most industries, PII that is generated in a country may not travel across borders even if the data remains within the same institution. Similarly, many organizations have Chinese walls across departments. These limitations prevent effective collaboration around data. Imagine a situation where multinational Bank A decides not to work with a customer in Country A, due to the risky nature of the customer’s transactions. Since regulations prevent data sharing across countries, even within the same bank, the risky customer could instead open an account in Country B, no questions asked. Enigma can help solve these types of serious business risks for multinational institutions. This example combined with 1) has significant implications in the fields of social and humanitarian work, where initiatives like refugee management and predictive epidemiology require the utmost care for individuals’ PII.

3) Training models without revealing personal data

Building on 1), Enigma’s privacy protocol can be used to build predictive models on shared data sets. This enables companies to better target customers without revealing the customers’ identities. Imagine running a loyalty program for a credit card. You are interacting with similar loyalty programs at airlines and hotels to give your customers the best deals to stay competitive. Through 1), you can not only gain full visibility to your customers’ spending patterns across organizations in a privacy-preserving manner, but you can also use those insights to make sure you are offering the most appealing rewards to similar customer profiles.