Privacy-Oriented Collaborative Learning Systems

This paper addresses the problem of data sharing among multiple parties in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. In our solution, multiple parties use homomorphic encryption and digital envelope techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines.