Research On Privacy Protection In Governmental Data Sharing

With the increasing demand of governmental data sharing,the opening and sharing of governmental data has increasingly attracted the attention of governments.However,governmental data sharing may bring about citizen privacy leakage risk.It becomes a focus issue of government department and public concern on how to share governmental data while protecting citizen's privacy from leaking.This thesis studies the issue of privacy protection under the governmental data sharing scenario.The main research contents include:(1)Discussed the needs of governmental data sharing and privacy concerns,analyze the privacy issues in governmental data sharing and the current privacy protection methods for privacy issues.A privacy protection data sharing framework between government departments,government departments and the public based on blockchain is constructed.The detailed data sharing process for the framework is designed.It also analyses and illustrates its functions and advantages in promoting data sharing and opening of government departments and improving the level of public services.(2)Studied a cluster-based anonymity privacy protection table data sharing method.The method is proposed for the scenarios of sharing static governmental data.Firstly,the records in the table are clustered by k-medios clustering algorithm,then the data table is divided into multiple sub-tables according to the distance between the records.Secondly,divide the records in each sub-table by combining with the value of information loss and adjust the anonymous table data to make it not all the same of the value of sensitive attribute in the equivalence class.Finally,the sensitive attribute values are treated with differential privacy by adding noise.An example analysis and experimental comparison with the classical k-anonymity algorithm MDAV show the effectiveness and feasibility of the proposed algorithm.(3)Designed and implemented the governmental data sharing algorithm based on local differential privacy.Aiming at the real-time scene of sharing governmental data,a method of sharing governmental data based on local differential privacy is designed and implemented.The shared data provider perturbs the sensitive attribute value of each data record,then shares the disturbed information and the required parameters to the data requester,so that data requester can performs statistical correction and obtains statistical data.The example analysis prove that the data after local differential privacy processing has certain availability.It can not only solve the problem of the maximum background knowledge attack,but also resist the attack of attackers within the government and protect the privacy and security of citizens.The cluster-based anonymity privacy protection table data sharing method proposed in this thesis has general applicability.And the implemented data sharing method based on local differential privacy can be extended to anonymous voting,polling and other scenes.