Abstract

In recent years, the protection of personal data has become a central concern for governments, businesses, and individuals. The General Data Protection Regulation (GDPR), implemented by the European Union in May 2018, set a new standard for data privacy and security. This regulation aims to ensure that personal data is processed fairly, transparently and securely. In parallel, Hidden Markov Models (HMMs) have emerged as a powerful statistical tool for modelling stochastic processes in various areas. However, the application of HMMs in contexts involving sensitive personal data raises serious privacy and security concerns. GDPR compliance poses additional challenges to the secure implementation of these models, requiring organizations to adopt appropriate technical and organizational measures. This study explores the challenges and solutions to implementing secure computing of HMMs, addressing anonymization, encryption, protection against cyberattacks, and regulatory compliance.

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