Final answer:
Anonymization and pseudonymization are insufficient as sole measures against data privacy breaches because they may reduce data usefulness, are not applicable to all data types, can be re-identified when combined with other data, and do not integrate well with data science workflows.
Step-by-step explanation:
The measures of anonymization and pseudonymization are crucial for maintaining data privacy but are not foolproof protection methods against breaches of data security. Anonymization involves the removal of personally identifiable information where the individual cannot be identified directly or indirectly.
Pseudonymization is the process of replacing private identifiers with fake identifiers or pseudonyms, thus reducing the linkability of a dataset to an individual without additional information.
These methods are insufficient protections for several reasons. First, they can potentially destroy the usefulness of the data, especially in contexts where personal details are vital for the utility of the information. Second, they may only be effective in scenarios with certain kinds of personal information.
Third, and most critically, if the anonymous or pseudonymous data is combined with other publicly available data, it can be possible to re-identify the individuals, compromising their privacy.
In addition, these measures do not always integrate well into data science and machine learning workflows, potentially limiting their use in these increasingly important fields. When organizations collect and use data, secure and ethical management must involve a comprehensive approach that goes beyond just anonymization and pseudonymization to protect individual privacy.
Therefore, the correct option is 1) They destroy the usefulness of the data.