  Information leakage is becoming a critical problem as various information
becomes publicly available by mistake, and machine learning models train on
that data to provide services. As a result, one's private information could
easily be memorized by such trained models. Unfortunately, deleting information
is out of the question as the data is already exposed to the Web or third-party
platforms. Moreover, we cannot necessarily control the labeling process and the
model trainings by other parties either. In this setting, we study the problem
of targeted disinformation generation where the goal is to dilute the data and
thus make a model safer and more robust against inference attacks on a specific
target (e.g., a person's profile) by only inserting new data. Our method finds
the closest points to the target in the input space that will be labeled as a
different class. Since we cannot control the labeling process, we instead
conservatively estimate the labels probabilistically by combining decision
boundaries of multiple classifiers using data programming techniques. Our
experiments show that a probabilistic decision boundary can be a good proxy for
labelers, and that our approach is effective in defending against inference
attacks and can scale to large data.
