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Machine Learning as a Service (MLaaS) is a popular cloud-based solution forcustomers who aim to use an ML model but lack training data, computationresources, or expertise in ML. In this case, the training datasets aretypically a private possession of the ML or data companies and are inaccessibleto the customers, but the customers still need an approach to confirm that thetraining datasets meet their expectations and fulfil regulatory measures likefairness. However, no existing work addresses the above customers' concerns.This work is the first attempt to solve this problem, taking data origin as anentry point. We first define origin membership measurement and based on this,we then define diversity and fairness metrics to address customers' concerns.We then propose a strategy to estimate the values of these two metrics in theinaccessible training dataset, combining shadow training techniques frommembership inference and an efficient featurization scheme in multiple instancelearning. The evaluation contains an application of text review polarityclassification applications based on the language BERT model. Experimentalresults show that our solution can achieve up to 0.87 accuracy for membershipinspection and up to 99.3% confidence in inspecting diversity and fairnessdistribution.

Private Training Set Inspection in MLaaS