Generative AI has revolutionized modern machine learning by providing unprecedented realism, diversity, and efficiency in data generation. This technology holds immense potential for biometrics, including for securing sensitive and personally identifiable information. Given the irrevocability of biometric samples and mounting privacy concerns, biometric template security and secure matching are among the most sought-after features of modern biometric systems. This paper proposes a novel obfuscation method using Generative AI to enhance biometric template security. Our approach utilizes synthetic facial images generated by a Generative Adversarial Network (GAN) as "random chaff points" within a secure vault system. Our method creates n sub-templates from the original template, each obfuscated with m GAN chaff points. During verification, s closest vectors to the biometric query are retrieved from each vault and combined to generate hash values, which are then compared with the stored hash value. Thus, our method safeguards user identities during the training and deployment phases by employing the GAN-generated synthetic images. Our protocol was tested using the AT&T, GT, and LFW face datasets, achieving ROC areas under the curve of 0.99, 0.99, and 0.90, respectively. Our results demonstrate that the proposed method can maintain high accuracy and reasonable computational complexity comparable to those unprotected template methods while significantly enhancing security and privacy, underscoring the potential of Generative AI in developing proactive defensive strategies for biometric systems.

Split learning (SL) is a distributed deep-learning approach that enables individual data owners to train a shared model over their joint data without exchanging it with one another. SL has been the subject of much research in recent years, leading to the development of several versions for facilitating distributed learning. However, the majority of this work mainly focuses on optimizing the training process while largely ignoring the design and implementation of practical tool support. To fill this gap, we present our automated software framework for training deep neural networks from decentralized data based on our extended version of SL, termed Blind Learning. Specifically, we shed light on the underlying optimization algorithm, explain the design and implementation details of our framework, and present our preliminary evaluation results. We demonstrate that Blind Learning is 65% more computationally efficient than SL and can produce better performing models. Moreover, we show that running the same job in our framework is at least 4.5× faster than PySyft. Our goal is to spur the development of proper tool support for distributed deep learning.