Anomaly detection over differential preserved privacy in online social networks

The massive reach of social networks (SNs) has hidden their potential concerns, primarily those related to information privacy. Users increasingly rely on social networks for more than merely interactions and self-representation. However, social networking environments are not free of risks. Users are often threatened by privacy breaches, unauthorized access to personal information, and leakage of sensitive data. In this paper, we propose a privacy-preserving model that sanitizes the collection of user information from a social network utilizing restricted local differential privacy (LDP) to save synthetic copies of collected data. This model further uses reconstructed data to classify user activity and detect abnormal network behavior. Our experimental results demonstrate that the proposed method achieves high data utility on the basis of improved privacy preservation. Moreover, LDP sanitized data are suitable for use in subsequent analyses, such as anomaly detection. Anomaly detection on the proposed method's reconstructed data achieves a detection accuracy similar to that on the original data.