Design of a Sharing System Based on Privacy-Preserving Personal Data

In recent years, machine learning has been widely used to process large-scale data and train complex models. However, there are certain security risks in the construction process of machine learning models. Data trainers may infringe on the privacy of data owners and use personal data to train machine learning models without authorization. To truly prevent unauthorized training of personal data, this paper proposes a privacy-preserving personal data sharing system, called PPDS. And this paper adopts the combination of CP-ABE with outsourced decryption and secure multi-party computation to ensure the secure authorization and training of personal privacy data. In PPDS, data users can only obtain the trained models instead of the authorized data, effectively preventing the privacy leakage of personal data. Moreover, the experimental results show that PPDS is a safe and effective scheme to achieve personal data sharing.