Trustworthy AI Challenges of Adoption of Privacy Preserving ML @ IC Workshop

Machine learning (ML) relies on diverse and well-curated datasets, but obtaining them is challenging due to data protection regulations, low quality, and biases. Trustworthy Artificial Intelligence (TAI) addresses these issues with privacy-preserving, explainable, and fair model training. Privacy-preserving ML (PPML) ensures safe and robust AI systems. Challenges include scalable tools and incentives for participation. Approaches like differential privacy and homomorphic encryption can help secure distributed ML pipelines and protect privacy. This talk explores the state of PPML, its motivations, challenges, and necessary developments for broader adoption. Balancing privacy and ML model training is crucial. Overall, via ongoing research and development, Usynin aims to overcome the challenges surrounding PPML and foster the integration of privacy-enhancing techniques with other aspects of trustworthy AI.