Research On Data Privacy Protection Technology In Wearable Sensor Networks

With the development of electronic information technology and the arrival of the era of big data, wearable sensor devices have become a new wave of scientific technological. It promotes the electronics industry to produce more innovative. Wearable sensor devices collect a large number of users’information, improving the quality of life, and also face the risk of privacy disclosure. Due to the openness of the communication environment and the high degree of privacy in the wearable sensor network, the traditional encryption algorithm and data collection scheme cannot be applied directly. Therefore it is very important to establish a lightweight secure and efficient data transmission, storage and release mechanism for the widely applied of wearable sensor devices. 。Before designs the data transmission and storage scheme, this paper analyzes the algorithm based on clustering structure (CPDA) and the algorithm based on slicing (SMART). This paper discusses the advantages and disadvantages of these two kinds of data aggregation algorithms and shows the insufficient they have in the special wearable networks.On the basis of the above research, this paper constructs the network model, trust model and security model of wearable sensor device, taking the medical system as an example, and puts forward a new scheme based on homomorphic encryption. The security analysis and comparison show that the algorithm has good communication load and computation load. It can protect the user’s privacy in the process of data acquisition and storage, resisting node capture attack, data retransmission attack and camouflage attack.Secondly, this paper studies the privacy protection of data publishing and dig into the k-anonymous technology. The traditional method to achieve k-anonymous is the generalization of the quasi identifiers and the hidden of original data. The k-anonymity based on micro aggregation technology (MDAV) has better effect than the former. However the MDAV algorithm still faces the link attack, solving the problem of outliers in the traditional algorithm. In addition, the k-anonymous based on MDAV algorithm can only be used for the static data set. It costs a lot when the data set dynamically updates.Finally, this paper designs a DMDAV algorithm, according to the disadvantages of MDAV based k-anonymous technology, aiming to protect the privacy of the individual and reduce the loss of information. This algorithm also improves the effect of anonymous, reduces the risk of link attacks, and realizes the dynamic update of data table. The AMDAV algorithm and BMDAV algorithm are designed for the user’s sensitive attribute, which can be numerical or non-numerical. In view of the algorithm proposed in this paper, the performance analysis and comparison are made. The simulation results show that the algorithm has good results in the aspects of computational load, information loss and leakage risk. The algorithm also protects the user’s privacy in the process of data publishing.