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Faster Privacy-Preserving Location Proximity Schemes

  • Kimmo JärvinenEmail author
  • Ágnes KissEmail author
  • Thomas SchneiderEmail author
  • Oleksandr TkachenkoEmail author
  • Zheng YangEmail author
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11124)

Abstract

In the last decade, location information became easily obtainable using off-the-shelf mobile devices. This gave a momentum to developing Location Based Services (LBSs) such as location proximity detection, which can be used to find friends or taxis nearby. LBSs can, however, be easily misused to track users, which draws attention to the need of protecting privacy of these users.

In this work, we address this issue by designing, implementing, and evaluating multiple algorithms for Privacy-Preserving Location Proximity (PPLP) that are based on different secure computation protocols. Our PPLP protocols are well-suited for different scenarios: for saving bandwidth, energy/computational power, or for faster runtimes. Furthermore, our algorithms have runtimes of a few milliseconds to hundreds of milliseconds and bandwidth of hundreds of bytes to one megabyte. In addition, the computationally most expensive parts of the PPLP computation can be precomputed in our protocols, such that the input-dependent online phase runs in just a few milliseconds.

Keywords

Location privacy Proximity Secure computation Homomorphic encryption 
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Notes

Acknowledgements

We thank Per Hallgren for providing the raw data of his benchmarks for comparison. This work has been co-funded by the DFG as part of project E4 within the CRC 1119 CROSSING, and by the German Federal Ministry of Education and Research (BMBF) and the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within CRISP. This work has been also co-funded by the INSURE project (303578) of Academy of Finland and by National Natural Science Foundation of China (Grant No. 61872051).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of HelsinkiHelsinkiFinland
  2. 2.TU DarmstadtDarmstadtGermany
  3. 3.Singapore University of Technology and DesignSingaporeSingapore

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