Introduction

OpenMPCC is a project which aims to combine secure Multi Party Computation (MPC) with Trusted Execution Environments (TEE) and the cloud. This enables secure and seamless collaboration in distributed environments and ensures privacy and trust.

The combination of these different technologies allows for the efficient deployment of:

• Private Information Retrieval
• Private Machine Learning
• Private Analytics

Concrete use cases for these technologies are for example:

• Cyber Threat Detection
• Threat Correlation
• Data Retention
• Chatbots on restricted information

Cyber Threat Detection and Threat Correlation leverage the Private Analytics component. Through MPC and TEEs collaborators are able to securely exchange sensitive data and analyze it without compromising the raw information. This allows federal agencies to collaborate with agencies from other countries while protecting the privacy of innocent individuals and obfuscating their sources.

Data Retention leverages the Private Information Retrieval component and allows the secure encrypted storage of sensitive information. However, authorized personnel is still going to be able to retrieve data when legally required even in cases where nobody other than the authorized personnel is allowed to know which data was retrieved.

The Private Machine Learning setting as employed by Chatbots on restricted information allows for chatbots to securely process and provide insights from restricted information. Private Machine Learning preserves the confidentiality of the data while allowing the AI-powered analysis.