  Recommendation systems make predictions chiefly based on users' historical
interaction data (e.g., items previously clicked or purchased). There is a risk
of privacy leakage when collecting the users' behavior data for building the
recommendation model. However, existing privacy-preserving solutions are
designed for tackling the privacy issue only during the model training and
results collection phases. The problem of privacy leakage still exists when
directly sharing the private user interaction data with organizations or
releasing them to the public. To address this problem, in this paper, we
present a User Privacy Controllable Synthetic Data Generation model (short for
UPC-SDG), which generates synthetic interaction data for users based on their
privacy preferences. The generation model aims to provide certain privacy
guarantees while maximizing the utility of the generated synthetic data at both
data level and item level. Specifically, at the data level, we design a
selection module that selects those items that contribute less to a user's
preferences from the user's interaction data. At the item level, a synthetic
data generation module is proposed to generate a synthetic item corresponding
to the selected item based on the user's preferences. Furthermore, we also
present a privacy-utility trade-off strategy to balance the privacy and utility
of the synthetic data. Extensive experiments and ablation studies have been
conducted on three publicly accessible datasets to justify our method,
demonstrating its effectiveness in generating synthetic data under users'
privacy preferences.
