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Data quality and security issues are very closely related. To ensure a high level of reliability in distributed systems and resilience from external attacks, the process of consolidating distributed data is critical. For consolidated systems, the access process relies heavily on data preprocessing, which, in turn, allows them to be anonymized. The analysis of closely related processes of consolidation and anonymization allows us to offer a secure access platform for distributed data, which makes it possible to implement secure access systems that depend only on the type and format of the data. It turns out that in the program stack for working with data, optimization can be done only with the entire framework, but not with its components. In this paper we perform analysis of data security as a complex problem related to both data quality and system architectures used to protect personal data.

Digital transformation has affected many industries and has become a mega-trend in the information technology systems. However, the venture capital investment process is still rigid. The use of distributed ledger technology and artificial intelligence in the field of finance and investment requires a detailed framework and a rethinking of the interaction model of market participants. The article shows the DLT based approach with attached of F-BFT consensus to improve the efficiency of the investment process in the modern world.

The decentralized voting protocol based on the Lagrange interpolant construction is analyzed for the potential error detection in the set of shares. We suggest an algorithm for the error locator polynomial construction based on procedure of computation the determinants of the Hankel type generated by the sequence of special symmetric functions of the set of shares.