The General Data Protection Regulation (GDPR) is a European Union law that requires organizations to obtain consent from individuals before processing their personal data.

The primary goal of data protection in big data environments is to prevent unauthorized access to data, as big data often contains sensitive and confidential information.

Encryption is a common data security measure used to protect data in big data environments by converting it into an unreadable format.

Data anonymization is the process of removing personally identifiable information (PII) from data to protect individual privacy.

All of the above are best practices for handling sensitive data in big data environments.

Data breach is the term used to describe the unauthorized access, use, disclosure, alteration, or destruction of data.

The General Data Protection Regulation (GDPR) requires organizations to provide individuals with the right to access and correct their personal data.

Big data analytics is the term used to describe the process of collecting and analyzing large amounts of data to uncover patterns and insights.

All of the above are common challenges associated with data privacy and protection in big data environments.

Encryption is the term used to describe the process of converting data into a format that cannot be easily understood or accessed without a key.

All of the above are best practices for implementing data privacy and protection in big data environments.

Data pseudonymization is the term used to describe the process of replacing sensitive data with fictitious or synthetic data to protect individual privacy.

The General Data Protection Regulation (GDPR) requires organizations to appoint a data protection officer (DPO).

Copyright infringement is the term used to describe the unauthorized copying, transmission, or distribution of copyrighted material.

All of the above are best practices for protecting data privacy in big data environments.