Efficient Privacy-Preserving Jaccard Similarity Evaluation Over Multisets for Secure Collaborative Data Analysis

This paper addresses privacy and security concerns in collaborative data analysis by proposing a new method for computing Jaccard Coefficient (a mathematical measure comparing similarity between two sets). The proposed protocol protects sensitive information like intersection and union cardinalities (counts of shared and combined elements) while maintaining high accuracy and computational efficiency, and can be enhanced further using cloud-assisted encryption to improve performance by 25.5% to 30.4%.