A compressive sensing–based adaptable secure data collection scheme for distributed wireless sensor networks

Zhen Liu,Yi-Liang Han,Xiao-Yuan Yang

doi:10.1177/1550147719856516

Abstract

Toward the goal of high security and efficiency for data collection in wireless sensor network, this article proposed an adaptable secure compressive sensing–based data collection scheme for distributed wireless sensor network. It adopted public key cryptography technology to solve the key distribution problem, and compressive sensing over finite fields to reduce the communication cost of data collection. Under hardness of decisional learning with errors problem on lattice, it can ensure indistinguishability against chosen ciphertext attack (IND-CCA1) security scheme for collected data on the extranet and indistinguishability against chosen plaintext attack security for data during the process of distributed collection on the intranet. Owing to the similar linear structure for lattices and compressive sensing, data encryption collection can be all in the form of efficient linear operations, and internode data aggregation can be in the form of addition operation.

Highlights

BackgroundWireless sensor networks (WSN) is considered as a bridge between human society and physical world
In a large-scale network, data transmissions are usually fulfilled through multi-hop routing from individual sensor nodes to the sink node, which causes a lot of redundant transmissions
We proved that the piecewise ciphertext of each node can preserve indistinguishability against chosen plaintext attack (IND-CPA) security of the data on intranet networks; each node completes the secure data collection during the transmission process

Summary

Background

Wireless sensor networks (WSN) is considered as a bridge between human society and physical world. To solve security challenges for CS-based data collection scheme, a feasible method is to let measurement matrix be a symmetric key, which is just known by both encrypted and decrypted parties. This ideal was formally studied in RB08,4 who regarded an original signal to be sampled as plaintext and its random measurements as ciphertext. Let GetMatr(m,n) is a polynomial time algorithm, when inputs security parameters (m, n), it outputs a random measure matrix F 2 Zm2 3 n, where m, n 2 Z and 0\m\n. Let RecSig(F, y) is a polynomial time algorithm, when inputs parameters (F, y), it outputs signals x 2 Zn2, where F 2 Zm2 3 n is a measure matrix, and it meets y = Fx 2 Zm2. Detailed proof can be seen in Theorem 6.3 in MP12

A CS-based adaptable secure data collection scheme for WSN

Encryption measurement and in-network aggregation

Data recovery

Conclusion