Anonymizing Shortest Paths in the Cloud

• Main Authors: Jia-wei Chen, 陳嘉葳
• Other Authors: none
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/72940444627019178710

碩士 === 國立高雄大學 === 資訊管理學系碩士班 === 102 === Preserving privacy on various forms of published data has been studied extensively in recent years. In particular, shortest distance computing in the cloud, while maintaining neighborhood privacy, attracts latest attention. To preserve one-neighborhood privacy, current approach requires the calculation of all-pairs shortest paths in advance, which is time consuming for large graphs. In this work, we propose two new privacy protection concepts, (1) k-skip shortest path privacy and (2) sensitive shortest path privacy. Combining k-skip shortest path, vertex hierarchy and bottom up partitioning, the proposed techniques provide efficient partitioning and query processing. Numerical experiments demonstrating the characteristics of the proposed approach have been conducted, and results show that the proposed techniques can efficiently construct the shortest path sub graphs for the cloud environment and provide efficient queries on shortest path distances.