Analysis of Privacy Leakage Risks in the Context of Security Threats Associated with Digital Device Fingerprinting

In the modern field of data processing, operations are often conducted within distributed systems, where information of individual users is shared across multiple servers. In such a context, users aim to protect their privacy despite the inevitability of transmitting their device parameters to servers, which occurs almost automatically. One of the main risks is the transmission of device hash identifiers (so-called “digital fingerprints”) along with private statistical information, which can lead to the near-complete de-identification of users by external organisations. This risk is exacerbated by the potential use of additional statistical data that can be associated with a user. This study proposes an analysis of existing methods for assessing the level of privacy and the effectiveness of various algorithms in maintaining differential privacy. The paper explores approaches to assessing the leakage of private data in scenarios where the security threat is associated with the computation and transmission of digital fingerprints of devices, representing a significant interest in developing more reliable data protection methods in distributed processing systems.