Two-Dimensional Private Set Intersection in Big Data

With the advent of the era of big data, the data scale being processed is more and more large, which brings new challenge to the design of privacy-preserving protocols. For the Private Set Intersection (PSI) problem, when the sizes of sets is large, keeping its efficiency and scalability is becoming very difficult. In addition, elements in these sets are likely to be twodimensional data, which usually contains an attribute and its corresponding weight. That increases the difficulty to solve the problem. Some creative protocols were proposed to settle the PSI problem recently. However, these protocols can only protect the privacy of the one-dimension elements of users´ private sets. And when the elements are two-dimensional, they are not work. In this paper, we propose an efficient and scalable protocol for sets which have two-dimensional elements. Thorough security analysis indicate that our scheme is effective and efficient.