Research Program 4 (R4)
Privacy-preserving and secure AI
The goal of FCAI’s Research Program Privacy-preserving and secure AI is to develop methods for privacy-preserving machine learning and artificial intelligence, especially based on differential privacy. Privacy-preserving and secure AI contributes mainly to FCAI research objective Trust and ethics (objective 2). Moreover, strong privacy preservation will ease the problem of data scarcity (closely related to objective 1, data efficiency) through encouraging more data sharing.
We are very active in developing differentially private machine learning methods, especially for Bayesian machine learning used in Agile Probabilistic AI. Our work also covers cryptographic and secure multi-party computation techniques for ensuring the security and privacy of the training of AI systems and their use in prediction. We cover a number of applications from health to generic deep learning and differentially private data release.
Examples of publications:
Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, and Samuel Kaski. 2021. Privacy-preserving data sharing via probabilistic modeling. Patterns 2(7): 100271.
Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. 2021. Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021).
Coordinating professor: Antti Honkela – antti.honkela at helsinki.fi
People
The groups of following PIs take part in the Research Program Privacy-preserving and secure AI. If you would like to join this program, please contact the coordinating professor.
Jukka Corander, University of Helsinki
Antti Honkela, University of Helsinki – coordinating professor
Pan Hui, University of Helsinki
Samuel Kaski, Aalto University
Arto Klami, University of Helsinki
Jaakko Lehtinen, Aalto University, NVIDIA
Janne Lindqvist, Aalto University
Pekka Marttinen, Aalto University
Sasu Tarkoma, University of Helsinki
Nikolaj Tatti, University of Helsinki
Aki Vehtari, Aalto University
Fundamental AI Research
Joint methodological goal
AI-assisted decision-making, design and modeling →
Research Programs
Probabilistic AI →
Simulators →
Deep learning →
Privacy and security →
Interactive AI →
Autonomous AI →
AI in society →
Highlight Programs
Modeling tools →
Health →
Service assistant →
Atmospheric →
Materials →
Sustainability →