Title: Quantum Privacy-Preserving Data Analytics,
Abstract: Data analytics (such as association rule mining and decision tree mining) can
discover useful statistical knowledge from a big data set. But protecting the
privacy of the data provider and the data user in the process of analytics is a
serious issue. Usually, the privacy of both parties cannot be fully protected
simultaneously by a classical algorithm. In this paper, we present a quantum
protocol for data mining that can much better protect privacy than the known
classical algorithms: (1) if both the data provider and the data user are
honest, the data user can know nothing about the database except the
statistical results, and the data provider can get nearly no information about
the results mined by the data user; (2) if the data user is dishonest and tries
to disclose private information of the other, she/he will be detected with a
high probability; (3) if the data provider tries to disclose the privacy of the
data user, she/he cannot get any useful information since the data user hides
his privacy among noises.