In the blockchain industry, an increasing number of market participants are looking for privacy-enabled solutions to secure their digital assets without compromising their ease of access.

Multi-party computation offers this solution using cryptographic techniques. Read on as we discuss how multi-party computation works and its applications on blockchains. 

What Is Multi-Party Computation?

Multi-party computation (MPC), or secure multi-party computation (SMPC), is a cryptographic protocol allowing multiple parties to jointly assess a computation while maintaining the privacy of their information and any secret data they wish to keep private.

Since blockchain networks involve data distribution across networks, MPC is a crucial piece of the puzzle in addressing the data privacy challenge. 

Before the 1980s, cryptography practices only involved concealing information. The introduction of multi-party computation in the 80s allowed the concealment of partial information while performing calculations using data from multiple sources. Today, multi-party computation has varied applications in the secure storage and transfer of digital assets.

How Does Multiparty Computation Work? 

The secure multi-party computation protocol uses additive secret sharing to divide and distribute private information among independent parties. There are no direct interactions between the information sources and the recipient. 

Take the example of an average salary problem where a party wishes to find the average salary based on the salaries of three employees. If one party earns $40,000, the protocol can divide the amount into three parts: $68,000, $32,000, and -$60,000, which add up to $40,000. 

The SMPC protocol then randomly divides the three parts among the three parties. The same process applies to the other two parties, leading to a total of nine data pieces that hold no meaning separately. Calculating the average salary is now possible without knowing what each party earns.  

Applications of Secure Multiparty Computation

SMPC complements existing blockchain protocols by enhancing privacy and security through additional features. The features present opportunities for various blockchain applications. 

Elimination of Single Point of Failure

The protocol protects the cryptographic keys from misuse and access by bad actors. SMPC allows storing cryptographic keys in software, offering alternatives to existing hardware solutions, which have their unique challenges. Under multiparty computation, multiple entities can apply uniform usage policies, allowing consistent security measures. 


The pseudonymous nature of the web3 industry can lead to trust issues. With SMPC, different parties can collaborate and share information without lifting the veil of pseudonymity. The rules of a particular protocol can require certain private information to promote trust. The parties can readily provide the information without reservation about other parties accessing it. 


Using SMPC ensures that even if bad actors collect the information, they cannot put the pieces back together to access the private information. Given the decentralized nature of blockchain technology, SMPC protocols enable each entity to secure its data instead of storing data in a central location. 

Regulatory Compliance

With many dApps facing varied regulatory hurdles, SMPC offers a solution to comply with data privacy regulations such as GDPR. 

Third-Party Elimination

Parties go through third parties to ensure their data remains safe and everyone involved fulfills their part. However, with the costs and delays that come with involving intermediaries, SMPCs eliminate the need to engage them. 

Limitations of Secure Multiparty Computation

There are some disadvantages of SMPC protocols. 

Communication Costs

For secret sharing to work, the network of all the parties must exist, and the transfer of information can lead to high communication costs. Unlike centralized systems, where a centralized hub receives and stores the data, SMPCs involve pieces of information sent to each party within the network. 

Computational Speed

Enhancing information privacy and security on SMPCs requires the generation of random numbers. As the number of parties and data size increase, the computation process becomes slow, leading to output delays. 

The Bottom Line

MPC technology aligns with the privacy and security expectations of the web3 industry. Organizations such as ZenGo, Coinbase, and Fireblocks have introduced wallets that utilize MPC technology. The technology minimizes reliance on cryptographic keys to access information. 

The technology has the potential to improve data security with multiple use cases. However, factors such as computational speed present a challenge to utilize the technology where speed and cost are priorities. This presents a scaling problem; time will tell how the technology advances to address the challenge.