Demo -- MaLFraDA: A Machine Learning Framework with Data Airlock

ABSTRACTTraining machine learning algorithms on sensitive, illegal to possess, and psychologically harmful data is challenging because researchers have to do training without handling the data. Moreover, the nature of the data imposes strict control, monitoring, and examination of all the activities involved, including communication, execution, and release of algorithms, datasets, outputs, and results. In this regard, this work proposes a new multi-zoned framework called MaLFraDA. MaLFraDA has soft air gaps between its zones to isolate and control communication in and out of the framework. Besides, it includes (i) a vetter to investigate and approve incoming model/algorithm, and outgoing information, (ii) encrypted data vaults, and (iii) airlock instances for secure execution/computation. MaLFraDA, with an extension, runs popular distributed machine learning algorithms such as federated and split learning using multiple data custodians.