Title :
Privacy-Oriented Collaborative Learning Systems
Author :
Zhan, Justin ; Matwin, Stan
Author_Institution :
Univ. of Ottawa, Ottawa
Abstract :
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.
Keywords :
cryptographic protocols; data encapsulation; data privacy; groupware; support vector machines; data exchange; data sharing; digital envelope technique; homomorphic encryption; learning support vector machines; linear function; polynomial function; privacy-oriented collaborative learning systems; private data set; secure protocol; sigmoid kernel function; Collaboration; Collaborative work; Cryptography; Data mining; Data privacy; Kernel; Machine learning; Protocols; Support vector machine classification; Support vector machines;
Conference_Titel :
Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
1-4244-0099-6
Electronic_ISBN :
1-4244-0100-3
DOI :
10.1109/ICSMC.2006.384776