Translation can infringe some of the data protection principles in GDPR. It can violate confidentiality, storage limitation, purpose limitation, and data minimization. To prevent these from happening, language data anonymization has become an essential part of the translation workflow.
Translation can infringe some of the data protection principles in GDPR. It can violate confidentiality, storage limitation, purpose limitation, and data minimization. To prevent these from happening, anonymization has become an essential part of the translation workflow.
The anonymization procedure is not limited to the removal of direct identifiers that might exist in a dataset. In a more aggressive approach, other data (secondary information like family relations, nationality, job description, etc.) could be used to identify a person and may require anonymization as well.
Anonymizing source texts addresses most of the issues in data protection. However, a more complex process of anonymization is required in machine translation.
By integrating data anonymization processes within the translation workflow, service providers can ensure that machines process data in a secure way that increases translation quality while maintaining compliance with data privacy laws.
Translation companies must always ensure the safe and secure use of data they process. Incorporating anonymization in their translation workflows enables them to avoid confidentiality breaches and privacy issues.
Governments are the biggest manufacturer of data. To keep private and confidential information secure, they must utilize anonymization tools in every aspect of data collection, processing, and sharing.
Businesses, regardless of size, need to share information in the most secure way and to comply with privacy laws. When it comes to handling corporate data, we are diligent in ensuring that data on Finances, Taxes, Employees, Customers, Suppliers for Businesses, is protected from outside intrusion or interference while retaining confidentiality and security.
Companies in the legal sector rely highly on precise language. By utilizing NLP and other AI tools, they can improve the quality of legal work and increase productivity. To keep the language as precise as possible but still protect sensitive client names, anonymization becomes an essential element in any NLP process.
Healthcare, banking, finance, insurance, and telecommunication are among the highly-regulated industries that are mandated to keep consumer data protected. Anonymization helps maintain the confidentiality of data and preserve its integrity.
