AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (785 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

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

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.
Show Author Information

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

secure two-party computationprivacy-preservingwireless sensor networksscalar productdistance calculationprivacy homomorphism

References

[1]
Yu C., Yao D., Li X., Zhang Y., Yang L. T., Xiong N., and Jin H., Location-aware private service discovery in pervasive computing environment, Information Sciences, vol. 230, pp. 78–93, 2013.
Crossref Google Scholar
[2]
Xiao M., Huang L., Xu H., Wang Y., and Pei Z., Privacy preserving hop-distance computation in wireless sensor networks, Chinese Journal of Electronics, vol. 19, no. 1, pp. 191–194, 2010.
Google Scholar
[3]
Yao A., How to generate and exchange secrets, in Proceedings of The 27th Annual Symposium on Foundation of Computer Science, Toronto, Canada, 1986, pp. 162-167.
[4]
Lu R., Lin X., and Shen X., SPOC: A secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 3, pp. 614–624, 2012.
Crossref Google Scholar
[5]
Liu L., Wu C., and Li S., Two privacy-preserving protocols for point-curve relation, Journal of Electronics, vol. 29, no. 5, pp. 422–430, 2012.
Crossref Google Scholar
[6]
Hazay C. and Lindell Y., Efficient Secure Two-Party Protocols: Techniques and Constructions. Springer, 2010.
[7]
ElGamal T., A public key cryptosystem and a signature scheme based on discrete logarithms, IEEE Transactions on Information Theory, vol. IT-31, no. 4, pp. 469–472, 1985.
Crossref Google Scholar
[8]
Paillier P., Public-key cryptosystems based on composite degree residuosity classes, in Proceedings of the 17th International Conference on Theory and Application of Cryptographic Techniques, Prague, Czech Republic, 1999, pp. 223-238.
Crossref
[9]
Amirbekyan A. and Estivill-Castro V., A new efficient privacy-preserving scalar product protocol, in Proceedings of the Sixth Australasian Conference on Data Mining and Analytics, Gold Coast, Australia, 2007, pp. 209-214.
[10]
Luo Y., Huang L., Chen G., and Shen H., Privacy-preserving distance measurement and its applications, Chinese Journal of Electronics, vol. 15, no. 2, pp. 237–241, 2006.
Google Scholar
[11]
Zhong H., Sun Y., and Yan F., Protocol for privacy-preserving space closest-pair of points, Computer Engineering and Applications, vol. 47, no. 4, pp. 87–89, 2011.
Google Scholar
[12]
Rane S., Sun W., and Vetro A., Privacy-preserving approximation of L1 distance for multimedia applications, in IEEE International Conference on Multimedia and Expo (ICME), Singapore, 2010, pp. 492-497.
[13]
Du W. and Zhan Z., Building decision tree classifier on private data, in Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, Maebashi Terrsa, Japan, 2002, pp. 1-8.
[14]
He L., Huang L., Yang W., and Xua R., A protocol for the secure two-party quantum scalar product, Physics Letters A, vol. 376, pp. 1323–1327, 2012.
Crossref Google Scholar
[15]
Vaidya J. and Clifton C., Privacy preserving association rule mining in vertically partitioned data, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002, pp. 639-644.
[16]
Li S. and Dai Y., Secure two-party computational geometry, Journal of Computer Science and Technology, vol. 20, no. 2, pp. 258–263, 2005.
Crossref Google Scholar
[17]
Conti M., Willemsen J., and Crispo B., Providing source location privacy in wireless sensor networks: A Survey, IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1238–1280, 2013.
Crossref Google Scholar
[18]
Jian Y., Chen S., Zhang Z., and Zhang L., Protecting receiver-location privacy in wireless sensor networks, in Proceeding of IEEE Infocom, 2007, pp. 1955-1963.
[19]
Mahmoud M. and Shen X., A cloud-based scheme for protecting source-location privacy against hotspot-locating attack in wireless sensor networks, IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 10, pp. 1805–1817, 2012.
Crossref Google Scholar
[20]
Yang B., Yu Y., and Yang C., A secure scalar product protocol against malicious adversaries, Journal of Computer Science and Technology, vol. 28, no. 1, pp. 152–158, 2013.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 21 Issue 4,
August 2016
Pages 385-396
DOI: 10.1109/TST.2016.7536716
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

623

Views

49

Downloads

37

Crossref

N/A

Web of Science

46

Scopus

3

CSCD

Altmetrics
Received: 06 May 2016
Accepted: 31 May 2016
Published: 11 August 2016
© The author(s) 2016
Return