Delegated Secure Sum Service for Distributed Data Mining in Multi-Cloud Settings

An increasing number of businesses are migrating their IT operations to the cloud. Likewise there is an increased emphasis on data analytics based on multiple datasets and sources to derive information not derivable when a dataset is mined in isolation. While ensuring security of data and computation outsourced to a third party cloud service provider is in itself challenging, supporting mash-ups and analytics of data from different parties hosted across different services is even more so. In this paper we propose a cloud-based service allowing multiple parties to perform secure multi-party secure sum computation using their clouds as delegates. Our scheme provides data privacy both from the delegates as well as from the other data owners under a lazy-and-curious adversary (semi-honest) model. We then describe how such a secure sum primitive may be used in various collaborative, cloud-based distributed data mining tasks (classification, association rule mining and clustering). We implement a prototype and benchmark the service, both as a stand-alone secure sum service, and as a building block for more complex analytics. The results suggest reasonable overhead and demonstrate the practicality of carrying out privacy preserved distributed analytics despite migrating (encrypted) data to pos- sibly different and untrusted (semi-honest) cloud services.