An Alternative to Big Tech Breakup: Secure, Private Data-Sharing

We all know that policymakers, technologists, and business people have varying opinions on how Big Tech should be handled in light of today’s antitrust laws. About two-thirds of Americans say they want Big Tech companies broken up. Others -like Bill Gates– think breaking up large companies will make little to no impact and actually hurt consumers. Is there a solution that sits somewhere in between the two extremes?

Viktor Mayer-Schönberger, Professor of Internet Governance and Regulation at Oxford, believes that there is — and the solution is a “progressive data-sharing mandate” between Big Tech and smaller competitors.

In a June 2019 interview with the MIT Technology Review, Mayer-Schönberger outlines his reasoning on why data-sharing could actually be more effective than traditional trust-busting:

Innovation is moving at least partially away from human ingenuity, toward data-driven machine learning. Those with access to the most data are going to be the most innovative, and because of feedback loops, they are becoming bigger and bigger, undermining competitiveness and innovation. So if we force those that have very large amounts of data to share parts of that data with others, we can reintroduce competitiveness and spread innovation.

His idea targets the root of why Big Tech companies hold so much market power. Big Tech companies generate massive amounts of data, and these data create protective moats around their businesses. Don’t break the companies up, argues Mayer-Schönberger, just build bridges over their moats. (He’s also not the only one believes this would work.)

Near the end of his interview, Mayer-Schönberger talks about implementation barriers and privacy concerns to his solution. He recognizes that his data-sharing “isn’t solving privacy issues” as this mandate would likely involve shipping around anonymized data, which has been proven to be even less anonymous than we thought. In fact, it could be a consumer privacy disaster. There could, however, be a way around this – and the answer could be Secret Computing®.

With Secret Computing®, Big Tech companies could securely share their internal data with smaller competitors without ever sending or revealing their sensitive data in the process. Smaller competitors could compute on Big Tech data troves without ever seeing the row-level data and without ever ingesting the data, both of which would reduce privacy and security concerns altogether. Secret Computing® could facilitate a data-sharing mandate while maintaining the highest privacy and security standards.

Who wins with this solution?

Policymakers: Effectively reduce market power in Big Tech without the legal and procedural complexities of unwinding established mergers.

Big Tech: Keep businesses intact while promoting competition, innovation, and privacy.

Consumers: Data is used to enhance their market freedom, not to benefit monopolies. Take back the power to choose platforms that better suit their privacy preferences.

Want to learn more about how Secret Computing® is helping organizations build privacy-first systems? Contact us today.