reidentifikatsioonile

Reidentification, also known as reidentification or reidentification risk, refers to the process of identifying individuals in a dataset that have been anonymized or de-identified. This can occur when seemingly anonymous data is combined with other data sources that contain identifying information. The primary concern with reidentification is the potential for privacy breaches, as it can lead to the disclosure of sensitive personal information.

Reidentification risk is a significant issue in data privacy and security. It can happen in various contexts, such as healthcare, social media, and government databases. For example, a study published in the journal Science in 2008 demonstrated that 87% of the U.S. population could be uniquely identified using just three pieces of information: zip code, gender, and date of birth. This highlights the potential for reidentification even when data appears to be anonymized.

To mitigate reidentification risk, several techniques can be employed. These include k-anonymity, which ensures that each record in a dataset is indistinguishable from at least k-1 other records; differential privacy, which adds noise to data to protect individual privacy; and data minimization, which involves collecting and storing only the data necessary for a specific purpose.

Reidentification is a critical topic in the fields of data privacy and security. As data collection and sharing become increasingly prevalent, understanding and addressing reidentification risks is essential to protect individual privacy and maintain public trust in data-driven systems.