About SunRISE

a PENTA Call 3 project

SunRISE objectives

  • Machine learning on the edge nodes, for IoT security analytics and anomaly detection

  • Cloud platform applying machine learning techniques for sharing relevant security data

  • Homomorphic encryption as privacy enhancing technology for Industry 4.0

  • Manufacturing technologies for uniquely secure low-footprint ASICs

SunRISE targets a crucial point in future IoT systems: a comprehensive chain of security evidence gathering and dissemination. Leveraging on recent advances in semiconductor manufacturing, machine learning, and privacy-preserving technologies, SunRISE targets:

  • Implementation of novel Privacy-Preserving Techniques (PPT) for Machine Learning (ML)

  • Development of hardware accelerators for cloud and edge computing

  • High volume production of immutable, hard-coded, unique identities to secure IoT devices in CMOS 200nm technology

  • Design of system and communication architectures enabling security by design

Machine Learning Technologies

The following ML approaches are investigated

  • Homomorphic Encryption (HE)

  • Federated Machine Learning (FedML)

  • Secure Multiparty Computation (MPC)

  • Differential Privacy (DP)