Privacy-Assured Large-Scale Navigation from Encrypted Approximate Shortest Path Recommendation

  • Zhenkui Shi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


As the fast-paced market of smart phones, navigation application is becoming more popular especially when traveling to a new place. As a key function, shortest path recommendation enables a user routing efficiently in an unfamiliar place. However, the source and destination are always critical private information. They can be used to infer a user’s personal life. Sharing such information with an app may raise severe privacy concerns.

In this paper, we propose a practical navigation system that preserves user’s privacy while achieving practical shortest path recommendation. The proposed system is based on graph encryption schemes that enable privacy assured approximate shortest path queries on large-scale encrypted graphs. We first leverage a data structure called a distance oracle to create sketch information, and we further add path information to the data structure and design three structured encryption schemes. The first scheme is based on oblivious storage. The second scheme takes advantage of the latest cryptographic techniques to find the minimal distance and achieves optimal communication complexity. The third scheme adopts homomorphic encryption scheme and achieves efficient communication overhead and computation overhead on the client side. We also evaluated our construction. The results show that the computation overhead and communication overhead are reasonable and practical.


Private navigation Distance oracle Oblivious storage PIR Homomorphic computation 



This work was supported by the Natural Science Foundation of China (Project No. 61572412) and the Microsoft Azure Research Grant.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceCity University of Hong KongHong Kong, S.A.R.China
  2. 2.City University of Hong Kong, Shenzhen Research InstituteShenzhenChina

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