Research On Privacy Protection Technology Of Publication For Wearable Device Data

With the constant breakthroughs on key information technology,wearable devices have rapidly developed its shape and functionality,becoming prevalent in people's daily life.Limited by their calculate power and storage capacity,a big quantity of data will be saved in clouds.Based on the needs of business cooperation and scientific research,a data owner of wearable devices should send the data to a third part or post on Internet.How to protect the individual's privacy while remaining the information availability is a focusing academic field.This paper has illustrated the privacy protection technologies,and especially summarized the protection models and basic concepts in publishing privacy field.In this article,the wearable device publishing model is divided into two parts:static and dynamic processing.In the static process,in light of the shortages of MAA-SEA(Micro Aggregation Algorithm Sensitive Attribute Entropy)algorithm,this paper has proposed FMAA-SEA model.And based on that,paper has proposed DSR-DAGUmodel which supporting dynamic data update.At last,this paper has further proposed DSR-DAGU model in skewed data situation.The main work of this thesis is organized as follows:(1)In static process,in light of the shortages of MAA-SEA algorithm,this this paper has proposed FMAA-SEA model.The clustering cost value FPV is calculated by data availability parameter and privacy protection parameter.And the concept of attribute weight is also introduced to adjust the emphasis of privacy protection according to the specific scenarios.(2)In the dynamic process,based on the data characteristic in wearable devices,high upload frequency and low upload quantity,and the algorithm mentioned above,this paper has proposed the dynamic attribute changing and group updating algorithm which supported data stream DSR-DAGU.The Cache Table proposed by this paper will restrain the information loss caused by data update and ensure the availability of updated data.And the Sensitive Attribute Update mechanism guarantee that privacy information is not disclosed while the information loss is lowest.At last,the Laplacian noise mechanism further enhance the level of privacy protection.(3)Based on the basic thought of(n,t)-closeness and the concept and calculation method of the distance of probability distribution,this paper has further proposed DSR-DAGUmodel in skewed data situation.Two possible forms of data skewness in publishing privacy are raised in the corresponding chapters.The FPV value is adjusted flexibly during the process of micro aggregation,and the clustering strategies are changed reasonably due to the specific data situation.Then the simulation and comparison of the proposed model and algorithm are carried out in the end of the corresponding sections.The simulation results show that the proposed model's and algorithm,s rationality and effectiveness by computational complexity,information loss,number of false data and privacy protection utility.Finally,this paper summarizes the research results,points out the problems in the research process,and the future research directions are also discussed.In this paper,we use 9 figures,18 tables,63 references.