AUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems

Abstract

Spurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity : how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO , an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server’s answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.