A privacy model that's useful to protect data in certain sharing scenarios (e.g. within an organization or with trusted partners). The issue: anonymizing direct identifiers isn't enough if individuals may be re-identified through quasi-identifiers (such as ZIP or post code, date of birth, etc) when linked to other available data sets. k-anonymity ensures that you cannot identify a single individual from a data set, no matter what other information you hold about them.
To achieve k-anonymity, there need to be at least k other individuals in the data set that share the same identifying attributes. If that isn't the case, k-anonymity can be achieved by generalizing the data. (e.g. rather than showing an exact birthday, the data shows a year; rather than an age of 23, a range of 20-30).

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Data de-identification 101 Webinar

K - anonymity: An Introduction

K-anonymity is a key concept that was introduced to address the risk of re-identification of anonymised data through linkage to other datasets.

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