  To protect user privacy in data analysis, a state-of-the-art strategy is
differential privacy in which scientific noise is injected into the real
analysis output. The noise masks individual's sensitive information contained
in the dataset. However, determining the amount of noise is a key challenge,
since too much noise will destroy data utility while too little noise will
increase privacy risk. Though previous research works have designed some
mechanisms to protect data privacy in different scenarios, most of the existing
studies assume uniform privacy concerns for all individuals. Consequently,
putting an equal amount of noise to all individuals leads to insufficient
privacy protection for some users, while over-protecting others. To address
this issue, we propose a self-adaptive approach for privacy concern detection
based on user personality. Our experimental studies demonstrate the
effectiveness to address a suitable personalized privacy protection for
cold-start users (i.e., without their privacy-concern information in training
data).
