DAG: A General Model for Privacy-Preserving Data Mining

DAG: A General Model for Privacy-Preserving Data Mining

IEEE Transactions on Knowledge and Data Engineering

10.1109/TKDE.2018.2880743

https://doi.org/10.1109/TKDE.2018.2880743

Sin Teo, Jianneng Cao, Vincent Lee

DAG, Secure, Operator, Privacy, and Data Mining

12 November 2018

S. G. Teo, J. Cao and V. C. S. Lee, "DAG: A General Model for Privacy-Preserving Data Mining," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 40-53, 1 Jan. 2020, doi: 10.1109/TKDE.2018.2880743.

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. SMC has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc – they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, *, /, and power). Our model is general – its operators, if pipelined together, can implement various functions, even complicated ones. The experimental results also show that our DAG model can run in acceptable time.

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