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Tsinghua Science and Technology 2016, 21(4): 385-396 https://doi.org/10.1109/TST.2016.7536716
Open Access | Issue | Published: 11 August 2016

Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks

Show Author's Information Haiping Huang( ),  Tianhe Gong,  Ping Chen,  Reza Malekian,  Tao Chen
Nanjing University of Posts and Telecommunications, Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China.
Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield 0028, South Africa.
Keywords:
wireless sensor networks, privacy-preserving, secure two-party computation, scalar product, distance calculation, privacy homomorphism
Cite this article:
Huang H, Gong T, Chen P, et al. Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks. Tsinghua Science and Technology, 2016, 21(4): 385-396. https://doi.org/10.1109/TST.2016.7536716
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Abstract Full text About this article

Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks (WSN), secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side’s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol (PPSPP) for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol (HEEDP) without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.


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About this article

Secure Two-Party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks

Show Author's information Haiping Huang( ), Tianhe Gong, Ping Chen, Reza Malekian, Tao Chen
Nanjing University of Posts and Telecommunications, Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China.
Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield 0028, South Africa.

Abstract

Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks (WSN), secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side’s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol (PPSPP) for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol (HEEDP) without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.

Keywords: wireless sensor networks, privacy-preserving, secure two-party computation, scalar product, distance calculation, privacy homomorphism

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Publication history
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Publication history

Received: 06 May 2016
Accepted: 31 May 2016
Published: 11 August 2016
Issue date: August 2016

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© The author(s) 2016

Acknowledgements

The authors would like to thank the anonymous reviewers of this paper for their objective comments and helpful suggestions. This work was sponsored by the National Natural Science Foundation of China (No. 61373138), the Natural Science Key Fund for Colleges and Universities in Jiangsu Province (No. 12KJA520002), the Key Research and Development Program of Jiangsu Province (Social Development Program) (No. BE2015702), the Postdoctoral Foundation (Nos. 2015M570468 and 2016T90485), the Sixth Talent Peaks Project of Jiangsu Province (No. DZXX-017), the Fund of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201516), and the Science and Technology Innovation Fund for Postgraduate Education of Jiangsu Province (No. KYLX15_0853).

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