The Internet of Things (IoT) is changing people's consuming behavior and business processes. Aiming at the information privacy and security of IoT devices, this paper proposes a location information protection model for IoT devices based on blockchain technology. Firstly, this model records device identification with the help of blockchain technology to ensure that the information of IoT devices cannot be tampered. Then the distributed Hash table (DHT) network is implemented based on white list technology and the device location information is XOR processed to hide the network topology of the IoT. Finally, the sensitive attributes of the data are generalized according to k-anonymous algorithm to provide users with regional information statistics services. The experimental results show that the model can effectively hide device location information, provide customized regional statistics service and protect user information security.
SHE Wei, CHEN Jiansen, GU Zhihao, TIAN Zhao, XU Li, LIU Wei
. Location Information Protection Model for IoT Nodes Based on Blockchain[J]. Journal of Applied Sciences, 2020
, 38(1)
: 139
-151
.
DOI: 10.3969/j.issn.0255-8297.2020.01.011
[1] Albino V, Berardi U, Dangelico R M. Smart cities:definitions, dimensions, performance, and initiatives[J]. Journal of Urban Technology, 2015, 22(1):3-21.
[2] Mukhopadhyay S C. Wearable sensors for human activity monitoring:a review[J]. IEEE Sensors Journal, 2014, 15(3):1321-1330.
[3] Weber R H. Internet of Things-new security and privacy challenges[J]. Computer Law&Security Review, 2010, 26(1):23-30.
[4] Zhang Z K, Cho M C Y, Wang C W, et al. IoT security:ongoing challenges and research opportunities[C]//2014 IEEE 7th International Conference on Service-Oriented Computing and Applications. IEEE, 2014:230-234.
[5] Durden T. Wikileaks Unveils' Vault 7':The Largest Ever Publication of Confidential CIA Documents; Another Snowden Emerges."by Zerohedge. Mar (2017).
[6] Yu M, Zhang J, Wang J, et al. Internet of Things security and privacy-preserving method through nodes differentiation, concrete cluster centers, multi-signature, and blockchain[J]. International Journal of Distributed Sensor Networks, 2018, 14(12):1550147718815842.
[7] Liu H, Li X H, Pei B, et al. Distributed k-anonymous location privacy protection scheme based on blockchain[J/OL]. Chinese Journal of Computers, 2019, 42(5):942-960.
[8] Yin C, Xi J, Sun R, et al. Location privacy protection based on differential privacy strategy for big data in industrial internet of things[J]. IEEE Transactions on Industrial Informatics, 2017, 14(8):3628-3636.
[9] Crosby M. Blockchain technology:beyond bitcoin[J]. Applied Innovation, 2016, 71(2):6-10.
[10] Konstantinos C, Devetsikiotis M. Blockchains and smart contracts for the Internet of Things[J]. IEEE Access, 2016, 4(5-10):2292-2303.
[11] Kosba A, Miller A, Shi E, et al. Hawk:the blockchain model of cryptography and privacypreserving smart contracts[C]//2016 IEEE Symposium on Security and Privacy (SP). IEEE, 2016:839-858.
[12] Maymounkov P, Mazieres D. Kademlia:a peer-to-peer information system based on the XOR metric[C]//International Workshop on Peer-to-Peer Systems. Berlin, Heidelberg:Springer, 2002:53-65.
[13] Crosby S A, Wallach D S. An analysis of bittorrent's two Kademlia-based dhts[R]. 2007.
[14] Moritz S, En-Najjary T, Biersack E W. A global view of kad,[C]//Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. ACM, 2007:117-122.
[15] Kim S K, Ma Z, Murali S, et al. Measuring Ethereum network peers[C]//Proceedings of the Internet Measurement Conference, 2018. ACM, 2018:91-104.
[16] Starnberger G, Kruegel C, Kirda E. Overbot:a botnet protocol based on Kademlia[C]//Proceedings of the 4th International Conference on Security and Privacy in Communication Netowrks. ACM, 2008:13.
[17] Niu B, Li Q, Zhu X, et al. Achieving k-anonymity in privacy-aware location-based services[C]//IEEE Conference on Computer Communications. IEEE, 2014:754-762.
[18] Sweeney L. k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(5):557-570.
[19] Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10(5):571-588.
[20] Wang S, Hu Q, Sun Y, et al. Privacy preservation in location-based services[J]. IEEE Communications Magazine, 2018, 56(3):134-140.
