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

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Mobile Ad-hoc and Sensor Networks (MSN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 747))

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Abstract

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.

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Acknowledgement

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

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Hong Kong, S.A.R., China

    Zhenkui Shi

  2. City University of Hong Kong, Shenzhen Research Institute, Shenzhen, 518057, China

    Zhenkui Shi

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  1. Zhenkui Shi
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Correspondence to Zhenkui Shi .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Liehuang Zhu

  2. Nanjing University, Nanjing, China

    Sheng Zhong

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Shi, Z. (2018). Privacy-Assured Large-Scale Navigation from Encrypted Approximate Shortest Path Recommendation. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_14

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  • DOI: https://doi.org/10.1007/978-981-10-8890-2_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8889-6

  • Online ISBN: 978-981-10-8890-2

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