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The secure scalar product (or dot product) is one of the most used sub-protocols in privacy-preserving data mining. Indeed, the dot product is probably the most common sub-protocol used. As such, a lot of attention has been focused on coming up with secure protocols for computing it. However, an inherent problem with these protocols is the extremely high computation cost-especially when the dot 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, the authors present ways to efficiently compute the approximate dot product.
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