Data protection in heterogeneous big data systems

Modern Big Data systems are notable for their scale and, often, their distributed organization. A feature of a number of Big Data systems is the use of heterogeneous data processing tools. These are different DBMS and streaming processing tools with different data granularity. The input information may be repeatedly fragmented and re-grouped as it moves between storage locations. In this case, the problems of data integrity, access control and auditing cannot be solved by traditional methods. This paper considers the security of heterogeneous Big Data systems based on distributed ledger technologies and verifiable zero-knowledge operations.