An Efficient Approximate Protocol for Privacy-Preserving Association Rule Mining

The secure scalar product (or dot, product) is one of the most used sub-protocols in privacy-preserving data milling. Indeed, the clot product is probably the most; common sub-protocol used. As such, a lot of attention leas been focused on coming up with secure protocols for computing it. However, all inherent problem with these protocols is the extremely high computation cost-especially when the clot product needs to be carried out over large vectors. This is quite common in vertically partitioned data., and is a real problem. In this paper, we present; writ's to efficiently compute the approximate dot product. We implement the clot product protocol and demonstrate the quality of the approximation. Our clot product protocol call be used to securely and efficiently compute association rules from data vertically partitioned between two parties.