Research On Verifiable Privacy Protection And Multidimensional Data Query In IoT

With the rapid development of Io T-related industries,privacy protection methods for the Io T have received widespread attention in various fields.Among various current examples of Internet of Things applications,hierarchical wireless sensor networks are an important part of the Internet of Things,and range query is a widely used query technology in hierarchical wireless sensor networks.However,while protecting the privacy of sensitive data,there are still problems such as incomplete attack type considerations,low efficiency,and high false positive rates when processing range queries for multidimensional data.Through research on the privacy and query result integrity of multi-dimensional data range queries in wireless sensor networks in the Internet of Things,this thesis proposes a new type of verifiable privacy-protected multi-dimensional data range query protocol(Middle distance-Query,MD-Query).The main research contents of this thesis are as follows.Aiming at the security and efficiency of multi-dimensional sensitive data in the range query process,the MD-Query protocol proposes a mid-range contrast query method based on eigenvalue construction.This method mainly constructs characteristic values of multi-dimensional sensitive data,and converts the multi-dimensional range parameter range.The range standard of multi-dimensional data is obtained from the characteristic value and the converted value.The value of the range standard is used to determine whether the data meets the query request.Since the eigenvalues are unforgeable and irreversible,the medium-distance contrast query method can ensure the security of multi-dimensional sensitive data,and the eigenvalues and eigenvectors generated by this method occupy less memory,consume less communication costs,and can more effectively meet need for high-efficiency queries.Aiming at the verifiability of the query results,the MD-Query protocol proposes an association verification scheme to verify the integrity of the query results.This solution mainly generates unique correlation check objects based on multi-dimensional sensitive data,and then encrypts the transmission after connecting with the data.After decrypting the query results,it determines whether the query results are deleted or forged based on the recovered multi-dimensional sensitive data set and correlation check object set.The association verification scheme can effectively judge the integrity of the query results.Furthermore,this thesis also considers multiple types of attacks including easy-to-ignore collusion attacks and probabilistic attacks,analyzes the privacy of the MD-Query protocol,and addresses network initialization,data transfer,and requests during range queries.The complexity analysis was performed in the four stages of query and query processing.Integrity analysis is performed for two types of integrity attack types,such as forging or deleting some multidimensional data items.The analysis proves that the MD-Query protocol can effectively prevent collusion attacks and probability attacks.Finally,the performance of the MD-Query protocol is evaluated through simulation experiments,and the results show that the MD-Query protocol has a lower false alarm rate and communication consumption compared with some existing protocols.