DAG A General Model for Privacy-Preserving Data Mining

1. 2020 – 2021
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DAG: A General Model for Privacy-Preserving Data Mining
Abstract
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 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 Naı̈ve
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 DAG model
to two data mining tasks: kernel regression and Naı̈ve Bayes. Experimental results
show that 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 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 DAG model takes 12.38 sec.