How Multiparty Computation is Reshaping Data Privacy

How is Multiparty Computation Reshaping Data Privacy?

Sadly, bias against women happens every day in business, and the nebulous nature of bias makes it a particularly challenging problem to confront.

One of the most visible and important examples of bias against women is the gender pay gap. According to the Pew Research Center, American women earn about 84 percent of what their male colleagues earn. While almost everyone agrees it is a problem, solving the gender pay gap has one major sticking point: People want to keep their income private.

This balancing act between personal privacy and the public good — and many other problems like it — can be addressed using a technology called secure multiparty computation. This cryptography-based protocol involves multiple participants contributing sensitive data to a system that processes it to produce a result without ever revealing sensitive information to individual participants.


What is Secure Multiparty Computation?

Sensitive digital assets are often accessed through the use of a private key. While this is often an effective approach, the use of a single private key creates a single point of attack for hackers to exploit.

In multiparty computation, shares of a private key are distributed among the participants using a multiparty computation protocol, so that a single private key never exists. Each participant can then authorize the process in a distributed way, similar to the way multiple trumpet players playing a single note are needed to produce a musical chord.

Organizations looking to embrace multiparty computation don’t need to enter an entirely new world of data tools. The protocol allows for all the functionality offered by relational databases and conventional statistical analyses.

Multiparty computation is being increasingly adopted as awareness of the technology makes its way into mainstream society. At a 2018 event held by New America, Sen. Ron Wyden (D-Oregon) strongly advocated in favor of the technology for its ability to leverage sensitive data and “protect it at the same time.”


Recent Applications of Secure Multiparty Computation

There are many reasons why secure multiparty computation is a promising technology, and the versatile nature of the technology is already being seen in a number of use cases.

In one implementation that millions of people use every day, Google has been using multiparty computation to improve the predictive typing function on its mobile devices. Because users want the information they type to be kept private, the tech giant is using secure multiparty computation protocols to analyze typing data without exposing any text content created by its users.

Before leaving office in 2014, Boston Mayor Thomas Menino launched a number of initiatives designed to address the gender pay gap in his city. Unfortunately, organizations and their attorneys wouldn’t allow for the release of employees’ wage data. With help from computer scientists at Boston University, the City of Boston leveraged a multiparty computation solution that was able to provide insights on the gender pay gap in Boston.

Secure multiparty computation has also been used to avoid satellite collisions far above the Earth’s surface. Orbital data for satellites is highly sensitive information. Companies do not want to release this information for fear it could decrease their competitive advantage. Governments do not want to release orbital information out of concerns related to national security. In 2010, the Defense Advanced Research Projects Agency (DARPA) started a multiparty computation initiative for the sharing of satellite data, and the result is a proof-of-concept algorithm that is reportedly ready for adoption.


The Road Ahead

Secure multiparty computation is just beginning to reshape perceptions around private data sharing, and experts are promoting many tantalizing possibilities, particularly those related to benefit the greater public good.

Speaking at the New America event, Amy O’Hara, a senior researcher at the Stanford Institute for Economic Policy Research, called for the use of secure multiparty computation to address the opioid epidemic currently ravaging the United States. O’Hara argued that multiparty computation could address the legal considerations and logistical costs needed to combine data from many different entities, including law enforcement agencies, healthcare organizations, and tech companies like Google.

Also speaking at the event, former Census Bureau director Robert Groves said secure multiparty computation could be used for more informed policy-making decisions. Many data silos exist within the federal government and breaking down these silos means cutting through a lot of red tape. Groves argued that multiparty computation could break down silos to solve policy issues in a way that maintains the privacy of individual citizens and avoids misuse.


Leveraging Multiparty Computation with TripleBlind

Building in part on core multiparty computation principles, The TripleBlind Solution allows participants in a data collaboration to maintain privacy while performing authorized operations on shared assets. TripleBlind has been granted several patents for its innovations in this field, which significantly extend the state of the art. Our technology avoids the limitations of other privacy-enhancing technologies, enabling the operationalization of highly complex and unstructured data types, while preserving privacy and ensuring compliance with HIPAA and GDPR

Available as a software-only solution, our technology does not require any specialized hardware or IT infrastructure. It natively supports major cloud platforms and is available for download via cloud marketplaces.

If you’d like to learn more about our innovative solution, please contact us today.