Projects

Quantum technologies are steadily moving beyond purely laboratory-based demonstrations toward systems with practical relevance, promising to solve some complex problems more efficiently than classical computing, such as factoring large numbers in cryptography. However, the quantum revolution is progressing gradually, with the current Noisy Intermediate-Scale Quantum (NISQ) era serving as an important stepping stone toward fully fault-tolerant quantum computing (FTQC). At present, the inherent noise and limited scale of NISQ devices fundamentally constrain their reliability and scalability, constituting a significant barrier to the wider practical use of quantum computing.

The NISQEC project addresses this challenge by developing quantum error correction (QEC) methods for protecting quantum information in the NISQ era. The project is deliberately grounded in realistic assumptions about near-term quantum hardware. It focuses on QEC techniques that can operate effectively with fewer than 1,000 qubits or under limited entanglement resources. By analyzing concrete noise models, finite-blocklength codes, and practical decoding strategies, NISQEC aims to clarify which approaches can provide meaningful improvements in reliability under realistic resource constraints.

By advancing our understanding of the fundamental limits of quantum error correction in the NISQ era, NISQEC helps clarify how quantum technologies can be made more reliable in practice. The project supports the long-term development of scalable quantum systems while remaining closely aligned with the capabilities and limitations of today’s quantum hardware. All results will be shared openly through publications, conferences, and international collaboration.

Whenever you enter a prompt into one of the big AI services, the company owning the machine learning models can see your input. This prevents the use of such services if your data is confidential and must not be shared outside your own organisation. The PREMAL project aims to solve this problem using encryption, allowing owners of sensitive data to make use of external AI services while still maintaining full control over their data.

Special cryptographic algorithms, known as homomorphic encryption, have been designed to enable computations on encrypted data. A data owner can use these to encrypt their input before sending it to the company hosting the AI solution. The decryption key is never shared with anyone, ensuring that the AI company cannot learn anything about your data, but is still able to perform the machine learning computations on it. 

Methods for doing encrypted machine learning are known, but the homomorphic encryption incurs a high computational cost. The project aims to bring this cost down, without sacrificing the quality of the responses from the AI. Homomorphic encryption can be used for both training models on private data and making inferences on already trained machine learning models. The project’s output will be a thorough understanding of the tradeoff between cost and accuracy in encrypted machine learning and privacy-preserving AI, with a focus on more practical use cases.

This project centers on the development of a privacy-preserving biometric identification system designed to empower users with control over their digital credentials. By focusing on the responsible use of biometrics, the initiative seeks to bridge trust frameworks and overcome the technical and legal hurdles associated with cross-border identity verification. The primary aim is to establish a secure, decentralized storage architecture, ensuring that personal data remains unexposed.

At the heart of the research and development phase is the challenge of maintaining data integrity while processing information in an encrypted state. Simula UiB leads the critical applied cryptography work package, which involves defining Fully Homomorphic Encryption (FHE) schemes specifically tailored for facial recognition. This technical pursuit is paired with an analysis of how to integrate existing national ID digital signatures and verifiable computation methods to ensure every step of the biometric matching process is both secure and auditable at the protocol level.

The successful implementation of this technology promises significant benefits across several sectors, ranging from individual users to large-scale service providers. Users will enjoy a more secure, simplified verification experience with full sovereignty over their data. Broadly, the project aligns with international progress by contributing to the UN Sustainable Development Goals, specifically targeting innovations in digital infrastructure and the provision of legal identity for all by 2030.

The main focus in PeerL is to develop novel private and efficient distributed learning solutions. Distributed learning has several important use cases, e.g.,
within healthcare where it enables hospitals to collaboratively analyze sensitive patient data from individual patients’ wearables or health records without sharing the data, improving diagnostic models and providing personalized treatment recommendations while preserving patient privacy. Another important use case of special interest in PeerL is within insurance where companies can collaborate to train improved crime prevention systems while maintaining the confidentiality of sensitive information.

Of special interest for this use case is a measure of security against leakage of competitive insights, referred to as competitive privacy, a novel concept introduced in this project to quantify the leakage of competitive insights by the sharing of data. The fear of leaking competitive insights is currently considered the main blocker to the wide-spread practical deployment of collaborative learning methods in insurance companies, as well as in the financial sector more broadly, and results from this project will help removing this barrier.

Symmetric cryptography is finding new uses due of the emergence of novel and more complex computing environments, many of which are based on sophisticated Zero-Knowledge (ZK) and Multi-Party Computation (MPC) protocols. These new uses often call for dedicated symmetric algorithm designs, typically natively described over large finite fields of odd characteristic (rather than in binary fields). The COSINUS Associate Team will combine the expertises at COSMIQ-Inria and Simula UiB, to research and devise novel design and cryptanalytic techniques for this new breed of symmetric cryptography.

The goal of this project is to develop “certifiable” Secure Multi-Party Computation (SMPC) protocols that ensure that data points consumed by the protocol are derived from accredited sources. SMPC protocols in current use for, e.g., sharing information between competing entities, do not usually verify that the private data input to the protocol is legitimate. The collaborative laboratory will tackle this problem by fundamentally reworking security models for such protocols, and analysing how developments in anonymous credentials and zero-knowledge proofs (ZKPs) can be used to export trust to privacy-preserving computations.

The AI Risk project studies future tools with general AI, called tool AIs. Tool AIs based on the human neocortex answer difficult questions. The tools are the basis for different types of intelligent devices. There are risks associated with the tools themselves and the answers they provide. The goal is to design neocortex-based tool AIs without existential risk to humanity and then describe systems of tool-based devices providing answers with acceptable non-existential risk to users and other stakeholders.

The project’s first part is described in two published papers Tutorial on systems with antifragility to downtime and A thousand brains: toward biologically constrained AI. The second part studies the properties tool AIs need to eliminate existential risk and how to create tool-based antifragile systems with acceptable non-existential risk. The project’s third part explores other risks users face when using tool-based personal assistants.

Since we know that future quantum computers will break much used cryptographic primitives and protocols, there is a need for new solutions. The project’s goal is to develop cryptographic primitives and protocols that resist attacks by quantum computers, and that realize trusted and secure communication in IoT ecosystems.

A satellite network is the only cost efficient technology to collect data from, and control, remote sensors and other IoT devices, in order to protect and learn about resources all over the arctic land and ocean areas.  For example, such a network may provide low latency low data rate for underwater gliders that can cover large distances efficiently and occasionally surfaces to dump data and receive commands, or medium data rate communication for surface buoys that provide situational awareness both above and below the water. The main objective of the ALISA project is to design, develop and demonstrate frequency agnostic air interface for low power IoT messaging on the C/X, Ku, and Ka-bands for use on LEO, (MEO), HEO and (GEO) satellites. The project is a collaboration between Widenorth, Space Norway (Norsk Romsenter), NTNU, and Simula UiB. The role of Simula UiB is to design the error correcting codes for the air interface, and the security part of the communication system.

The project will explore advanced compression techniques of high bandwidth analogue signals for this scenario. The focus is to design and implement a bidirectional wideband RF over IP module with the target to support bandwidths up to 5 GHz. The targeted use-case scenarios are bi-directional VSAT communication and downlink from earth observation satellites. A main challenge in the project is the design and implementation of efficient compression algorithms of high bandwidth analogue signals, like Rice-Golomb coding and more advanced compressed sensing techniques like Xampling.

The term “distributed storage” denotes scalable and economically viable technology for storing humanity’s collective memory. It is unknown how to best design such distributed storage systems that are both robust against arbitrary failures, and secure against targeted attacks. The project addresses these issues through a theoretical approach guided by practical concerns.

The transition to cloud computing requires new security measures to protect valuable data no longer under the direct control of the owner. This joint project with NTNU studies cryptographic tools to protect data in the cloud against powerful attackers. The goals are to develop mechanisms to ensure the privacy of stored data and to allow secure computations with private data, without needing to trust the cloud provider.

LDPC codes have become a standard in wired and wireless communications, as well as in data storage. The principal reasons for the success of LDPC codes are their high resistance to noise and their efficient encoding and decoding algorithms. The goals of this project are to design more reliable codes, new decoding methods, and novel analytical tools to understand better the structure and performance of LDPC codes.