Abstract

The protection of privacy in the dissemination of statistical information is facing increasingly complex challenges. The growing volume, availability, and granularity of both public and private data, in combination with emerging technologies such as artificial intelligence and quantum computing, undermine the effectiveness of traditional methods—such as suppression or masking—for preventing reconstruction attacks on anonymized databases. This article introduces Differential Privacy (DP) as a mathematically rigorous framework for safeguarding data confidentiality, ensuring that statistical outputs released in the dissemination of information do not disclose sensitive details about specific individuals