MPC in Finance and Healthcare: Enabling Data Analysis While Protecting Privacy

Introduction

Multi-Party Computation (MPC) has revolutionized the way organizations from heavily regulated industries like finance and healthcare can collaborate on complex computational tasks while ensuring the confidentiality and integrity of their data. In this blog post, we'll delve into the world of MPC and explore its applications in finance and healthcare, highlighting the benefits, technical details, and real-world implications.

MPC Fundamentals

MPC is a cryptographic technique that enables multiple parties to jointly perform a computation on their private data without revealing their individual inputs. This is achieved through the use of homomorphic encryption, which allows computations to be performed directly on the encrypted data, without the need to decrypt it first.

The MPC protocol typically involves three main components:

  • Private Inputs: Each party contributes their private input to the computation, which remains confidential throughout the process.
  • MPC Protocol: The parties agree on a shared protocol that defines how the computation will be performed, ensuring that the output is accurate and the inputs remain private.
  • Secure Computation: The parties execute the computation using the agreed-upon protocol, ensuring that the output is the result of the joint computation, without revealing any individual inputs.

Homomorphic Encryption

Homomorphic encryption is a key component of MPC, allowing computations to be performed on encrypted data. There are two main types of homomorphic encryption:

  • Somewhat Homomorphic Encryption: This type of encryption allows for a limited number of computations to be performed on the encrypted data, but the results are not necessarily accurate.
  • Fully Homomorphic Encryption: This type of encryption enables arbitrary computations to be performed on the encrypted data, while maintaining the confidentiality and integrity of the data.

Some of the most popular homomorphic encryption schemes include:

  • HElib: A open-source library for homomorphic encryption, developed by Microsoft Research.
  • SEAL: A homomorphic encryption scheme developed by Microsoft Research, which enables efficient computation on large datasets.

MPC Protocols

There are several MPC protocols that have been developed to enable secure computation across multiple parties. Some of the most popular protocols include:

  • Secure Multi-Party Computation (SMPC): A protocol developed by IBM, which enables secure computation across multiple parties using homomorphic encryption.
  • Garbled Circuits: A protocol developed by IBM, which enables secure computation across multiple parties using garbled circuits.
  • Homomorphic Encryption: A protocol developed by Microsoft Research, which enables secure computation across multiple parties using homomorphic encryption.

Real-World Applications

MPC has numerous real-world applications in finance and healthcare, including:

  • Risk Modeling: MPC can be used to enable multiple financial institutions to jointly calculate risk models, without revealing their individual transaction details.
  • Data Mining: MPC can be used to enable multiple institutions to jointly conduct data mining, without revealing their individual data sets.
  • Predictive Modeling: MPC can be used to enable research institutions to jointly develop predictive models, using shared patient data, while ensuring the confidentiality of individual patient records.

Case Studies

Finance

  • Risk Modeling: A group of banks can use MPC to jointly calculate risk models, without revealing their individual transaction details. This can help to reduce the risk of financial losses and improve the accuracy of risk assessments.
  • Data Mining: A group of financial institutions can use MPC to jointly conduct data mining, without revealing their individual data sets. This can help to identify patterns and trends that may not be apparent from individual data sets.

Healthcare

  • Predictive Modeling: A group of research institutions can use MPC to jointly develop predictive models, using shared patient data, while ensuring the confidentiality of individual patient records. This can help to improve the accuracy of disease diagnosis and treatment outcomes.
  • Clinical Trials: A group of researchers can use MPC to jointly conduct clinical trials, using shared patient data, while ensuring the confidentiality of individual patient records. This can help to accelerate the development of new treatments and improve patient outcomes.

Challenges and Best Practices

Challenges

  • Scalability: MPC protocols can be computationally intensive and may not scale well for large datasets or complex computations.
  • Communication Overhead: MPC protocols may require significant communication overhead, which can be a challenge in environments with limited bandwidth.
  • Security: MPC protocols require strong cryptographic primitives and secure implementation to ensure the confidentiality and integrity of the data.

Best Practices

  • Choose the Right Protocol: Select an MPC protocol that is suitable for your specific use case, taking into account the size and complexity of the data, as well as the computational resources available.
  • Implement Securely: Implement the MPC protocol securely, using strong cryptographic primitives and secure coding practices.
  • Test and Validate: Thoroughly test and validate the MPC protocol to ensure that it is functioning correctly and securely.

Conclusion

MPC is a powerful cryptographic technique that enables multiple parties to jointly perform complex computational tasks while ensuring the confidentiality and integrity of their data. In finance and healthcare, MPC has numerous applications, including risk modeling, data mining, and predictive modeling. While there are challenges associated with MPC, such as scalability and communication overhead, best practices can be followed to ensure secure and accurate computations.