POSTER: SecretSVM -- Secret Sharing-Based SVM for Preventing Collusion in IoT Data Analysis

Support vector machine (SVM) is widely used because of its efficiency in data processing. Single source data may not train a nice SVM, since a single entity isn't having enough data with adequate attributes. Thus, multiple source data need to share data to combine a dataset with different attributes, and then jointly train a classifier. However, outsourcing data to a cloud for training induces two security concerns, data privacy and collusion with the cloud and providers. In this paper, we consider a distributed scenario without any centralized party in which IoT data providers will jointly serve as the leader to obtain some parameters. We propose a privacy-preserving and collusion-free SVM (so-called SecretSVM)built from secret sharing and distributed consensus. Participants train intermediate values to provide the necessary interaction andprevent against collusion attacks.