The Root Cause of the Failure of American COVID-19 Governance Based on the Criticism of Liberal Democracy From Error-Tolerant Democracy

Liberal democracy cannot help America govern COVID-19 effectively for liberalism’s misunderstanding of the concept of liberty.Error-tolerantism divides liberty into the right to liberty in innovative fields,the right to be wrong as an original right,and the right to be right in non-innovative ones as sub-rights;rights come from mutual empowerment among people.The major defects of liberal democracy from the perspective of error-tolerant democracy constructed on error-tolerantism are as follows:The essence of election is to transfer people’s right to be wrong and corresponding right to be right to politicians,but the separation and balance of powers does not evaluate whether presidents,states,mayors,et al.,have exercised the power to be wrong reasonably,so that they could even abuse it in the COVID-19 governance,and did great harm to people’s human rights without any accountability or impeachment.Democratic governor’power to be wrong authorized by election was deprived by President Trump through issuing false information in the COVID-19 governance and encouraging people to protest against the anti-pandemic laws,which made liberal democracy in the United States threatened and COVID-19 out of control.

A Prerequisite for the Successful Governance of COVID-19 in the United States:Expansion of Liberal Democracy From Politics to Economy

The failure of COVID-19 governance in the United States of America is closely related to its economic inequality.According to the theory of error-tolerant democracy,after reflecting on the American financial crisis of 2008,liberty should be redefined from mutual empowerment under the background of regulation,which means that rights to liberty of entrepreneurs and financial capitalists in the public economy are empowered by the people,so they should empower and benefit the people,corresponding to regulation and redistribution respectively.During the COVID-19 pandemic,empowering and benefiting the people regarded as economic democracy has not been realized for neoliberal policies,leading to insufficient public funds to help citizens badly hurt by the disease.To restore the U.S.as the beacon of democracy,it should undertake the historical mission:expansion of liberal democracy from politics to economy so as to reduce unreasonable economic inequality and protect rights to life of more infected Americans.

Efective Error-Tolerant Keyword Search for Secure Cloud Computing

The existing solutions to keyword search in the cloud can be divided into two categories： searching on exact keywords and searching on error-tolerant keywords. An error-tolerant keyword search scheme permits to make searches on encrypted data with only an approximation of some keyword. The scheme is suitable to the case where users＇ searching input might not exactly match those pre-set keywords. In this paper, we first present a general framework for searching on error-tolerant keywords. Then we propose a concrete scheme, based on a fuzzy extractor, which is proved secure against an adaptive adversary under well-defined security definition. The scheme is suitable for all similarity metrics including Hamming distance, edit distance, and set difference. It does not require the user to construct or store anything in advance, other than the key used to calculate the trapdoor of keywords and the key to encrypt data documents. Thus, our scheme tremendously eases the users＇ burden. What is more, our scheme is able to transform the servers＇ searching for error-tolerant keywords on ciphertexts to the searching for exact keywords on plaintexts. The server can use any existing approaches of exact keywords search to search plaintexts on an index table.

Keyword Query over Error-Tolerant Knowledge Bases

With more and more knowledge provided by WWW, querying and mining the knowledge bases have attracted much research attention. Among all the queries over knowledge bases, which are usually modelled as graphs, a keyword query is the most widely used one. Although the problem of keyword query over graphs has been deeply studied for years, knowledge bases, as special error-tolerant graphs, lead to the results of the traditional defined keyword queries out of users＇ satisfaction. Thus, in this paper, we define a new keyword query, called confident r-clique, specific for knowledge bases based on the r-clique definition for keyword query on general graphs, which has been proved to be the best one. However, as we prove in the paper, finding the confident r-cliques is #P-hard. We propose a filtering-and-verification framework to improve the search efficiency. In the filtering phase, we develop the tightest upper bound of the confident r-clique, and design an index together with its search algorithm, which suits the large scale of knowledge bases well. In the verification phase, we develop an efficient sampling method to verify the final answers from the candidates remaining in the filtering phase. Extensive experiments demonstrate that the results derived from our new definition satisfy the users＇ requirement better compared with the traditional r-clique definition, and our algorithms are efficient.