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Can data mastering help repel electoral fraud?

While direct fraud is not especially high, the use of technologies to disrupt and seek to exert undue influence over public opinion remains high.

An early voting option has been provided this year to reduce crowding on election day due to Covid-19
An early voting option has been provided this year to reduce crowding on election day due to Covid-19 - Copyright AFP PATRICIA DE MELO MOREIRA
An early voting option has been provided this year to reduce crowding on election day due to Covid-19 - Copyright AFP PATRICIA DE MELO MOREIRA

Election integrity, security and accuracy are cornerstones of any thriving democracy. These considerations are also important to countries where elections have previous been under threat from cyber-fraud. One country with a recurrent concern with digital technology being harnessed to disrupt elections is the U.S.

Trying to defraud or disrupt the electoral process using digital means takes different forms, and it can involve the collection, distribution, modification, disruption, interference with, corruption, and degradation of information, in order to obtain a different outcome.

While the incidence of fraud and misconduct is infinitesimally rare in the U.S., the perception of voters that it happens is higher. While direct fraud is not especially high, the use of technologies to disrupt and seek to exert undue influence over public opinion remains high.

What can U.S. officials do to change concerns into confidence?

According to Keith Barnes, Head of Public Sector at Tamr there are several security measures that can be considered. Looking at one of these – data mastering – Barnes sets out a security strategy.

Barnes  proposes: “Governments can reduce threats to election infrastructure through data mastering. Next-generation data mastering that uses machine learning to provide clean, curated data helps fraud detection in the election process and increases the integrity of ballots.”

According to IBM, data mastering refers to the process by which an unmastered data source record is linked or merged with another master data record.

The data mastering process can either result in the creation of a new master data record, or the source data record is linked to an existing master data record. Through such interconnections a degree of security can be built.

As to how this might work in practice, Barnes continues: “Government agencies capture data about individuals and organizations, e.g., date of birth, school, and tax records. With next-generation data mastering, the government can use machine learning to consolidate, clean, and categorize data across agencies.”

Furthermore: “By doing so, states would be able to cross-reference voter registration rolls with other agencies, creating a cleaner, golden record for the voter roll. The key is for government agencies to share data while still protecting it, then use that data to help combat threats against the integrity of the electoral system.” This approach is not without its challenges, notes Barnes: “The hard part is getting government agencies to legally share the critical data needed to create the checks and balances that will give everyone confidence in safeguarding elections.”

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Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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