A multiple sensitive attributes data publishing method with guaranteed information utility

Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.

A fault-tolerant and scalable boosting method over vertically partitioned data

Vertical federated learning(VFL)can learn a common machine learning model over vertically partitioned datasets.However,VFL are faced with these thorny problems:(1)both the training and prediction are very vulnerable to stragglers;(2)most VFL methods can only support a specific machine learning model.Suppose that VFL incorporates the features of centralised learning,then the above issues can be alleviated.With that in mind,this paper proposes a new VFL scheme,called FedBoost,which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction.The server can build a machine learning model and predict samples on the union of coded data.The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved.Our scheme can support canonical tree-based models such as Tree Boosting methods and Random Forests.The experimental results also demonstrate the availability of our scheme.