  Recently, privacy issues in web services that rely on users' personal data
have raised great attention. Unlike existing privacy-preserving technologies
such as federated learning and differential privacy, we explore another way to
mitigate users' privacy concerns, giving them control over their own data. For
this goal, we propose a privacy aware recommendation framework that gives users
delicate control over their personal data, including implicit behaviors, e.g.,
clicks and watches. In this new framework, users can proactively control which
data to disclose based on the trade-off between anticipated privacy risks and
potential utilities. Then we study users' privacy decision making under
different data disclosure mechanisms and recommendation models, and how their
data disclosure decisions affect the recommender system's performance.
  To avoid the high cost of real-world experiments, we apply simulations to
study the effects of our proposed framework. Specifically, we propose a
reinforcement learning algorithm to simulate users' decisions (with various
sensitivities) under three proposed platform mechanisms on two datasets with
three representative recommendation models. The simulation results show that
the platform mechanisms with finer split granularity and more unrestrained
disclosure strategy can bring better results for both end users and platforms
than the "all or nothing" binary mechanism adopted by most real-world
applications. It also shows that our proposed framework can effectively protect
users' privacy since they can obtain comparable or even better results with
much less disclosed data.
