Zero-Knowledge Computation: Explain It Like I’m Seven

It’s all about trust and configurable privacy.

The resolution for blockchains in 2022 is all about integrating privacy via zero-knowledge solutions. Zero-knowledge — what’s all the hype about? Beyond security, interoperability and speed, in order for a blockchain to be successful, it needs privacy. But how much privacy and how can this work in tandem with the transparency and distributed trust that the blockchains promise?

We’ve all heard the endless stories of hacked blockchains and cryptocurrencies used to execute black market transactions without detection, giving a false idea of ominous privacy. This is not the case. Instead of anonymity, most blockchains offer pseudonymity, and to be pseudonymous is to use a false name or persona to hide your real identity. We don’t want blockchains to be anonymous. “If you think of it, true anonymity is the recipe for a perfect criminal network” Peter Frandsen, CTO at the Partisia Blockchain Foundation, puts it.

Furthermore, configurable privacy is vital in fields like healthcare, digital identity solutions, international payments and finance, international supply chain management, trade finance and any other complex transactional problems: i.e. keeping some data private while still making other data available.

Unencrypted public blockchains cannot provide the level of data security and confidentiality needed for use-cases in these fields because anyone can see all information on them with no restrictions whatsoever — which makes these platforms vulnerable with arms outstretched toward any opponent wanting access

Zero-Knowledge Proofs enable Privacy

This is where zero-knowledge proofs come into picture. Using these, blockchains can verify transactions without revealing the sensitive data being exchanged—which is precisely what makes them private. Zero-knowledge proofs allow one party to prove to another that they know a piece of a secret without revealing what that information actually is. In the context of blockchain, this could be used to prove that a transaction is valid without revealing the sender, receiver, or amount involved.

For example, two hospitals that want to share some patient data for trial inclusion, but keep all data about that patient private (think of TBC, where the disease is associated with social stigma in some countries of the world; or simply the need to maintain identity private of patients that are looking for a second medical opinion in the Western countries, and want to make sure the insurance company or the primary doctor don’t penalize them). By using zero-knowledge proof, the hospital can prove that the data (medical analysis result for example) is correct and belongs to the patient without revealing who the patient is. This way, both hospitals can be sure that the data is correct without breaching patient privacy.

But that’s about the only thing that ZK proofs do. They are limited because they only inform about a true or false result. In this sense, ZK proofs are trivial, their output is binary in a very complex world, with multiple parties involved, multiple complex inputs served, and multiple outputs requested.

Enter secure Multi-Party Computation (MPC) aka privacy-preserving or ZK computation.

The ultimate stage of zero-knowledge computation, MPC, allows not only to prove that something is true but also to compute the answer to a certain question without revealing any information about the input. ZK computations allow several parties to provide secret input, while the outputs can be larger, depending arbitrarily on all the secret inputs.

To understand how it works, let’s go back to the public-private medical data sharing example:

Take multiple pharma labs and multiple hospitals involved in researching a deadly neuro-muscular disease treatment. They are probably researching different molecules on different patients, looking for breakthroughs in the advancement or treatment of the disease. It usually takes years for any pharma lab to bring a drug to the market, since it needs to run different stages of a trial, keep information about the patients fully sealed, publish results, submit all results for review with the medical authorities etc. By the time another pharma lab working on a slightly modified solution gets access to the first labs’ results years have passed by. If they could share their research data timely, such as data aggregated results during different experimental trials, while keeping the information about the patients private, and still checking that the patients set has been selected according to strict eligibility criteria, etc. the data exchange could help bring to market treatments drugs much faster and potentially even improved synthesis treatments.

This is what MPC or zero-knowledge computation does. It can securely perform any computation on distributed data, by establishing a cluster of computing parties. For any computer program designed to do a specific computation, in this case determine the optimal treatment based on multiple trials results, MPC will compile on top of distributed encrypted patients data without the data ever leaving the servers they were on from the beginning, and hence never exposing any secret information it wasn’t supposed to reveal. This way, all labs can be sure that the data is correct without breaching patient privacy or confidentiality about their own proprietary interests.

An added benefit is that the zero-knowledge or MPC blockchain would be ultimately secure: the system does not leak the isolated result of any ongoing medical trials, nor the code of the smart contracts or their states. All that would be leaked are the intended outputs.

Real-world Zero-Knowledge computation use cases

• Basic cryptographic infrastructure such as key management for crypto wallets, from Partisia Blockchain project partner and co-founders from SBI Holding and Sepior
• On-chain secure order matching, where buyers and sellers of financial securities transactions outside of the exchanges are matched through highly specialized matching services i.e. off-exchange matching, like Cyberian from Tora.co
• Data brokerage for a decentralized social media platform that uses zero-knowledge and multiparty computation to protect user data, delivered by Partisia and Instars.com
• Other MPC applications include three auction solutions and a developed public-private healthcare data exchange.

To sum up, zero-knowledge computation or MPC is the utmost cryptographic orchestration of zero-knowledge. That’s why we at Partisia have named our token $MPC — because our team firmly believes in its power and ability to provide absolute privacy.

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