Private Training Set Inspection in MLaaS
Mingxue Xu Tongtong Xu Po-Yu Chen
Machine Learning as a Service (MLaaS) is a popular cloud-based solution for
customers who aim to use an ML model but lack training data, computation
resources, or expertise in ML. In this case, the training datasets are
typically a private possession of the ML or data companies and are inaccessible
to the customers, but the customers still need an approach to confirm that the
training datasets meet their expectations and fulfil regulatory measures like
fairness. However, no existing work addresses the above customers' concerns.
This work is the first attempt to solve this problem, taking data origin as an
entry 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 the
inaccessible training dataset, combining shadow training techniques from
membership inference and an efficient featurization scheme in multiple instance
learning. The evaluation contains an application of text review polarity
classification applications based on the language BERT model. Experimental
results show that our solution can achieve up to 0.87 accuracy for membership
inspection and up to 99.3% confidence in inspecting diversity and fairness
distribution.