01748nas a2200169   4500008003900000245008300039210006900122260000900191520116900200100001001369700002101379700001901400700002001419700002101439700001101460856010701471       2012                          d00aAUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.0 aAUDIO An Integrity Auditing Framework of OutlierMiningasaService  c20123 aSpurred 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.1 aR.Liu1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://kdd.isti.cnr.it/publications/audio-integrity-auditing-framework-outlier-mining-service-systems