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The Urgent Need for Data Minimization Standards

Data minimization, a key tenet in global data protection laws, lacks clear legal definition, impacting organizations’ confidence in their product’s compliance. Anonymizing data for compliance can be challenging due to misinterpretation and misleading claims about irreconcilable tensions with machine learning. A lack of consensus on de-identification standards threatens the trust and efficacy of technologies developed for responsible data use. Despite advancements in machine learning to identify personal data, without universally accepted standards, the potential to harness data safely for responsible innovation is under threat.

Source: www.cyberdefensemagazine.com –

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