a PENTA Call 3 project
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
The Importance of Data Privacy
The benefits of machine learning are nowadays used in various fields such as smart homes, healthcare, cloud infrastructure, but also in various other fields such as autonomous driving, . Machine learning has shown to be the state-of-the art solution for many safety critical applications (object detection, ) and security applications (). In the context of cybersecurity, machine learning is almost always part of the system solution for anomaly, intrusion detection, allowing to identify malicious e-mails and protects the user from fraud.
But all those promising advantages come at a high price, the vast amount of data collected about every individual can be easily misused to harm the victim. To give 3 examples in the scope of the project:
Energy communities: From fine grained electricity consumption data, it is possible to forecast future electricity consumption and detected anomalies in the consumption, e.g. if someone would intentionally tamper with the data. But it is also possible to observe customer behavior and infer information about when someone will most likely be at home and when not.
Healthcare: Modern approaches to detect cancer in MRI images are often based on ML algorithms, but the health data of a patient is very privacy-sensitive.
Cloud Infrastructure: In this use case various distributed data center containers are equipped with server hardware to offer cloud services to customers. The hardware is cooled down with a water cooling cycle. The heated water is supplied to
The SunRISE project aims to develop novel Privacy-Preserving Techniques (PPT) for Machine Learning. The following algorithms are investigated in the project:
Homomorphic Encryption (HE)
Federated Machine Learning (FedML)
Secure Multi-Party Computation (MPC)
Differential Privacy (DP)
Ethics of AI systems
NXP GE has taken its stance
In 2021 NXP GE has taken its stance and published the whitepaper The Morals of Algorithms, in which the importance of Data Privacy and NXP's vision for the coming years is explained.