Privacy Enhancing Computation

Privacy-enhancing computation (also known as secure multi-party computation) is a field of computer science that focuses on designing and implementing methods for multiple parties to jointly compute a function over their private inputs without revealing anything about the inputs to each other except for the output of the function.


Applications of privacy-enhancing computation include secure online voting, secure auctions, and secure machine learning. One example of an application of privacy-enhancing computation is homomorphic encryption, which allows computation to be performed directly on encrypted data without the need to decrypt it first. Another example is secure multiparty computation protocols such as SPDZ and Fairplay, which allow multiple parties to compute functions jointly over their private inputs without revealing them to each other.


Privacy-enhancing computation (PEC) refers to a set of technologies and techniques that enable individuals and organizations to perform computations on sensitive data without revealing the data itself to the computational party. PEC has a wide range of implementations, including:

case studies

Protect your data and privacy with privacy-enhancing computation.

Keep your sensitive information confidential and secure.

Use secure multi-party computation to perform calculations on data without revealing the underlying data to any of the parties involved.

Ensure that your data is only used for the intended purpose, without being shared or sold to third parties.

Use homomorphic encryption to perform calculations on encrypted data without ever decrypting the data.

Benefit from the increased security and privacy provided by privacy-enhancing computation in various applications, including finance, healthcare, and government services.

Connect with us to investigate how this pioneering technology - privacy-enhancing computation - can revolutionize your business or organization.
Try it today and witness the transformation it brings.