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 Naïve 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 Naïve 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.

中文翻译：

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DAG：隐私保护数据挖掘的通用模型

安全多方计算 (SMC) 允许各方在他们的输入上联合计算一个函数，同时对每个输入保密。它已广泛应用于具有隐私要求的任务，例如隐私保护数据挖掘（PPDM），以学习任务输出并同时保护输入数据隐私。然而，现有的基于 SMC 的解决方案是临时的——它们是为特定应用程序提出的，因此不能直接应用于其他应用程序。为了解决这个问题，我们提出了一个隐私模型$\mathsf {DAG}$有向无环图（有向无环图）由一组基本安全运算符（例如，+、-、×、/ 和幂）组成。我们的模型是通用的——它的算子，如果流水线化在一起，可以实现各种功能，甚至是像朴素贝叶斯分类器这样的复杂功能。它也是可扩展的——可以定义新的安全运算符来扩展模型支持的功能。对于案例研究，我们应用了我们的$\mathsf {DAG}$有向无环图两个数据挖掘任务的模型：核回归和朴素贝叶斯。实验结果表明$\mathsf {DAG}$有向无环图生成的输出与非私人设置几乎相同，其中多方只是公开他们的数据。实验结果还表明，我们的$\mathsf {DAG}$有向无环图 模型在可接受的时间内运行，例如，在内核回归中，当训练数据大小为 683,093 时，非私有设置中的一次预测需要 5.93 秒，而我们的 $\mathsf {DAG}$有向无环图 模型需要 12.38 秒。