| Summary: | With the continuous development of information technology in the era of big data,privacy problem has attracted more and more attention.Especially with the increasing popularity of mobile terminals,how to protect users' privacy information while releasing data is a major challenge at present.Previously,academic circle has proposed the center differential privacy technology that relies on a trusted third platform,but the condition that needs a trusted third platform is usually not valid in practical applications.On the basis of center differential privacy,localized differential privacy is further proposed.It can prevent privacy attacks from untrusted third platforms,and it still has a strong defensive effect against privacy attackers with abundant knowledge background.But markets often cater to the needs of service providers as well as users.In order to balance the contradiction between the two,how to accomplish the analysis tasks of service providers is a problem that must be solved.RAPPOR is a good mechanism to accomplish these tasks.It encrypts user data by using two random response mechanisms to ensure the strength of privacy protection.Lasso regression model is used to decrypt the encrypted data to ensure the accuracy of frequency feature extraction.In this paper,RAPPOR algorithm is applied to COVID-19 epidemic information collection,which can obtain real epidemic data while protecting the privacy of respondents.The dataset which includes people diagnosed with COVID-19 in the United States is used to simulate the RAPPOR mechanism and fits the real results to a high degree.RAPPOR algorithm realizes the localized differential privacy technology from theory to application,and effectively protects personal privacy.
|
|---|