On Private Scalar Product Computation for Privacy-Preserving Data Mining
Bart Goethals, Sven Laur, Helger Lipmaa and Taneli Mielikäinen
In: ICISC 2004, 2-3 Dec 2004, Seoul, Korea.
In mining and integrating data from multiple sources, there are many privacy and security issues. In several different contexts, the security of the full privacy-preserving data mining protocol depends on the security of the underlying private scalar product protocol. We show that two of the private scalar product protocols, one of which was proposed in a leading data-mining conference, are insecure. We then describe a provably private scalar product protocol that is based on homomorphic encryption and improve its efficiency so that it can also be used on massive datasets.