Research on personalised privacy-preserving model of multi-sensitive attributes

In order to protect user information from being leaked, it is imperative to improve the availability of published data and realise the safe and efficient information sharing. Aiming at the anonymous privacy-preserving of multi-sensitive attribute data release in logistics industry, this paper proposes a personalised privacy-preserving model of multi-sensitive attributes with weights clustering and dividing (PMSWCD) by analysing existing model. Firstly, according to the different needs of users, the corresponding weight is set for each sensitive attribute value to realise personalisation and then weighted clustering. Secondly, divide the records according to the weighted average value, and select records to establish a group that satisfies l-diversity. Finally, release data based on the idea of multi-dimensional bucket. Through experimental analysis, compared with WMBF algorithm, the release ratio of important data of PMSWCD algorithm proposed in this paper is significantly improved, reaching more than 95%, which improves the availability of data.