Differentiated privacy in AI is primarily at home in the fields of artificial intelligence, big data and smart data, as well as cybercrime and cybersecurity.
The term describes how personal data from users can be specifically protected when working with artificial intelligence (AI). Instead of treating all data with the same level of stringency, differentiated privacy looks more closely: Which information is particularly sensitive and which may be used otherwise? The goal is to adapt privacy protection flexibly and individually to the respective user and the use case.
For example: A health service uses AI to create individual fitness plans. Data such as step count or sleep times could be analysed anonymously, while particularly sensitive medical diagnoses remain strictly confidential and are not used for advertising purposes. This creates a balance between the useful utilisation of data and the safeguarding of personal privacy.
Differentiated privacy in AI is therefore an important approach for building trust, complying with legal requirements, and safely leveraging the benefits of intelligent systems.
Deutsch 
English (UK)