Bloom filter-based lightweight private matching scheme

Abstract

With rapid developments of mobile devices and online social networks,users of proximity-based mobile social networks (PMSN) could easily discover and make new social interactions with others,but they enjoyed this kind of conveniences at the cost of user privacy and system overhead,etc.To address this problem,a third party free and lightweight scheme to privately match the similarity with potential friends in vicinity was proposed.Unlike most existing work,proposed scheme considered both the number of common attributes and the corresponding priorities on each of them individually.The Bloom filter-based common-attributes estimation and the lightweight confusion binary vector scalar product protocol reduce the system overhead significantly,and can resist against brute force attack and unlimited input attack.The correctness,security and performance of overhead of proposed scheme are then thoroughly analyzed and evaluated via detailed simulations.

Keywords

• privacy matching
• Dice similarity coefficient
• Bloom filter
• confusion binary vector scalar product protocol