Research On Privacy-preserving Data Sharing Technology In Internet Of Things Environment

The rapid development of the Internet of Things(IoT)has brought about a large amount of data.The collection and rational use of the vast amount of Io T data can improve the intelligence level of the Io T,and thus further enhance the efficiency of the Io T,improve the Io T user experience.However,breaking the status quo of data islands,the collection of large amounts of Internet of things data need to consider how to deal with the potential privacy leakage problem during data sharing.Compared with the traditional Internet data sharing scheme,the limitations of Io T devices in data sensing,communication transmission,computation and battery life should be considered in data sharing in Io T environment.The simple transplantation of traditional privacy protection system can not fit the internet of things situation perfectly,it is necessary to design a privacy-preserving data sharing framework based on the characteristics of Io T to support efficient,secure and high-availability privacy-preserving data sharing.Based on the current data sharing framework in Io T,this paper designs a more general Io T privacy-preservingn data sharing framework and conducts research on the potential weaknesses during different data sharing scenarios under this framework,namely: data sharing between devices,outsourced data sharing and verifiable data sharing,the key research findings include:1)Aiming at the problem that it is difficult for Io T devices to establish mutual trust before data sharing,a multi-device pairing(RBMDP)scheme based on Received Signal Strength(RSS)with human-intervention is designed.In this scheme,Io T devices use recorded RSS data instead of vulnerable preset-keys as a root of trust to achieve mutual authentication between devices.The scheme also uses the randomness of AP’s hardware to securely generate the random seed used for session key generation before devices’ data sharing process.In addition,due to the low distance sensitivity of RSS data with human-intervention,the scheme supports the pairing of multiple devices,placed in different locations,within one pairing attempt.Finally,the security proof and experimental analysis show that the scheme proposed in this chapter has reliable security and acceptable efficiency.2)Aiming at the problem of low availability when outsourced data is encrypted,an outsourced data sharing scheme supporting range query(RQDPDR)and an outsourced data sharing scheme supporting multiple data usages(MUDPDP)are designed.The RQDPDR scheme uses Order-Preserving Encryption(OPE)to ensure that the cloud server can use the encrypted outsourced data to respond range queries.At the same time,the RQDPDR scheme adopts Differential Privacy(DP)technology to ensure the privacy of the response to the range queries.In addition,the RQDPDR scheme reduces the noise introduced by DP and improves the data availability through the use of post-processing procedure while ensuring the privacy of the outsourced data.The MUDPDP scheme uses the Additive Homomorphic Encrption(AHE)to ensure the privacy of the outsourced data while supporting the differential privacy noise addition process conducted by the cloud server over encrypted outsourced data.In addition,MUDPDP uses data generalization technology to construct different views of outsourced data to support different types of data requests,which further improves the availability of outsourced data.Both the safety proof and the experimental analysis show that the scheme proposed in this chapter has sufficient safety and feasibility.In addition,the experimental results also show that the post-processing procedure proposed in RQDPDR scheme can effectively reduce the total amount of noise introduced by DP,which means the scheme is with high usability.3)Aiming at the problem that the data is unknown when data sharing is privacypreserved,and the data is uncontrollable after sharing,a Verifiable Data Sharing(VREFL)scheme based on Linear Homomorphic Hash(LHH)is designed in the federated learning(FL)framework.In the VREFL scheme,the device determines whether the shared data obtained by itself is reliable by verifying whether the data calculation result conforms to the operation result of the linear homomorphic hash.The VREFL scheme also uses the value of the linear homomorphic hash corresponding to the shared data to construct a Merkel tree.Then,the traceability of the shared data and its related operations can be achieved by committing the root of the Merkel tree.In addition,the VREFL scheme designs a dynamically updated noise generation method based on the Chinese remainder theorem.Compared with the traditional way of generating noise through negotiation between devices,this method has lower communication overhead and higher robustness to Io T devices’ unstable connections.Finally,the security proof and extensive experimental results show that the VREFL scheme is secure,and its efficiency and communication overhead are acceptable.Through the study of the above key issues,this paper designed a data sharing framework in the Internet of things to solve the problem of privacy protection in data sharing,while improving the efficiency of data sharing,it maintains the availability of shared data in IOT and ensures the reliability of shared data in iot.It is an excellent,practical and privacy-protecting data sharing framework in iot.