What is Multi-Party Computation as-a-Service?

Multi-Party Computation as-a-Service (MPCaaS) is a cloud computing service that allows multiple parties to jointly perform computations on sensitive data without revealing any of the underlying data to each other. It is a form of secure computing that provides the means to perform complex calculations while preserving data privacy and confidentiality.

In MPC, the input data is divided into shares and distributed among the parties involved in the computation. Each party holds a share of the data, but no single party has access to the complete data. The parties then perform computations on their shares in a way that allows the final result to be computed without revealing the underlying data.

MPCaaS makes it possible for organizations to collaborate on complex computations without having to worry about data privacy and security. This makes MPC an attractive solution for various industries, including finance, healthcare, and government, where data privacy and security are critical.

With MPCaaS, the infrastructure, software, and computational resources required for MPC are provided as a service by a third-party provider. This eliminates the need for organizations to invest in and maintain their own MPC infrastructure, making it easier and more cost-effective to implement MPC.

In addition to providing secure computing, MPCaaS also offers other benefits, including scalability, flexibility, and ease of use. With MPCaaS, organizations can easily scale their computational resources as needed, without having to worry about the complexity of managing MPC infrastructure. MPCaaS also provides the flexibility to perform computations on a variety of platforms, including cloud, on-premises, and hybrid environments.

Imagine that you and your friends want to play a game of hide and seek, but you don't want any of your friends to see where you are hiding.

To make this possible, you agree to follow a special method. You each hide somewhere in the house, and then you take turns giving clues to the seeker about where you are hiding. But, even though you are giving the clues, none of your friends can see where you are hiding.

This is like MPCaaS. It allows several parties to work together on a task, without revealing their secret information to each other. The clues that you give to the seeker are like the computations that are performed in MPCaaS, and the places where you are hiding are like the sensitive data that is used in the computations.

With MPCaaS, a computer does the calculation, following the rules set by all the parties involved, just like the seeker follows the clues to find all the hidden players. The computer does the calculation, but it does not know what the underlying data is, just like the seeker does not know where each player is hiding.

In the prelude to the era of cloud computing, where data was guarded like a possessive dragon in isolated silos, Andrew Yao, a young Chinese computer scientist, emerged in the early 1980s as a visionary. Envisaging a future of secure collaboration, Yao introduced a groundbreaking protocol known as "Garbled Circuits," the inaugural stroke on the canvas of Multi-Party Computation (MPC). This innovative protocol allowed two parties, such as friends Alice and Bob, to jointly compute a function without divulging their private inputs. Think of it as constructing a circuit—a labyrinth of wires and gates—where Alice's grade transforms into instructions, and Bob's grade becomes the data flowing through. The result manifests without either party glimpsing the other's secrets. Yao's quest for secure collaboration gained momentum as brilliant minds like Oded Goldreich, Silvio Micali, and Avi Wigderson joined forces, expanding the two-party dance into a multi-party computation symphony. This collaborative effort birthed the term "Multi-Party Computation," a rallying cry for a new epoch of privacy-preserving computation, liberating data from the shackles of exposure. Today, MPC has transcended sci-fi fantasy, finding practical applications in real-world scenarios, ranging from secure auctions to collaborative fraud detection. Companies utilize MPC to train AI models on sensitive data without compromising privacy, and governments explore its potential for secure elections.

Healthcare: MPCaaS can be used in the healthcare industry to securely analyze large amounts of patient data for research purposes, without compromising patient privacy. For example, multiple hospitals can collaborate on a study to identify risk factors for a particular disease by contributing patient data, while the underlying data remains confidential.

Finance: MPCaaS can be used in the finance industry to perform secure financial computations, such as credit scoring and risk analysis, without revealing sensitive information about customers or transactions. For example, multiple banks can collaborate on a credit scoring model, while maintaining the confidentiality of their individual customer data.

Government: MPCaaS can be used by government agencies to securely process sensitive information, such as voting data or intelligence information, without revealing it to unauthorized parties. For example, multiple government agencies can collaborate on a security threat analysis, while preserving the confidentiality of their individual intelligence data.

• Secure Multi-Party Computation (SMPC): A technique for allowing multiple parties to perform a computation together, while keeping their inputs private.

• Metaverse-as-a-Service: A business model that offers metaverse-related services and solutions to clients.