The term „privacy-preserving computations“ is primarily found in the fields of Artificial Intelligence, Big Data and Smart Data, as well as cybercrime and cybersecurity. They describe methods with which personal data is particularly well protected during calculations and analyses, so that no confidential information leaks out.
The aim is for companies to gain valuable insights from large amounts of data without violating the privacy of individuals. Common technical approaches include, for example, encryption during computation or the splitting of data so that no one has access to all the details at the same time.
A simple example: A health insurance company wants to find out how often people of certain age groups visit the doctor. With privacy-preserving calculations, they can carry out this evaluation without ever seeing the exact names or personal data of the insured. The information is used in such a way that no conclusions can be drawn about individual people.
Such calculations are becoming increasingly important, especially when sensitive data needs to be processed. This ensures that innovation and data protection are no longer a contradiction.
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