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

作　　者：Haiping Huang Tianhe Gong Ping Chen Reza Malekian Tao Chen 

机构地区：[1]Nanjing University of Posts and Telecommunications,Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks [2]Department of Electrical,Electronic and Computer Engineering,University of Pretoria

出　　处：《Tsinghua Science and Technology》2016年第4期385-396,共12页清华大学学报（自然科学版（英文版）

基　　金：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 and2016T90485);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);the Science and Technology Innovation Fund for Postgraduate Education of Jiangsu Province(No.KYLX15 0853)

摘　　要：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.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.

关 键 词：secure two-party computation PRIVACY-PRESERVING wireless sensor networks scalar product distancecalculation privacy homomorphism 

分 类 号：TN929.5[电子电信—通信与信息系统] TP212.9[电子电信—信息与通信工程]