DAG: A General Model for Privacy-Preserving Data Mining

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Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. It 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 $\mathsf {DAG}$ 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 like Naive Bayes classifier. It is also extendable – new secure operators can be defined to expand the functions that the model supports. For case study, we have applied our $\mathsf {DAG}$ DAG model to two data mining tasks: kernel regression and Naive Bayes. Experimental results show that $\mathsf {DAG}$ DAG generates outputs that are almost the same as those by non-private setting, where multiple parties simply disclose their data. The experimental results also show that our $\mathsf {DAG}$ DAG model runs in acceptable time, e.g., in kernel regression, when training data size is 683,093, one prediction in non-private setting takes 5.93 sec, and that by our $\mathsf {DAG}$ DAG model takes 12.38 sec.