PRIVACY-PRESERVING DATABASE QUERIES AND DATA ANALYSIS

Abstract

The increasing volume of cloud storage data and distributed external data sources exacerbates the problem of ensuring confidentiality of access to data. First of all, this concerns government, financial and medical information systems, where personalized information about users is processed. Breaches of confidentiality in such systems can lead to serious legal and economic consequences, as well as the leakage of sensitive data. This research work provides a systematic review of modern models, methods and algorithms for confidential query processing.

The paper considers key concepts affecting differential privacy, kNN (k Nearest Neighbor) query processing algorithms and cryptographic protocols. Studies on the integration of blockchain technologies, ontology models with query confidentiality issues are identified. An analysis of existing approaches to query optimization taking into account confidentiality is carried out, the main trends are identified and promising research areas are determined.

The methodology of the systematic review complies with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard and the Vosviewer program for visualizing bibliometric networks. A comprehensive search was conducted in the Web of Science and ResearchRabbit databases using relevant keywords reflecting the specifics and objectives of the study. At the final stage of the analysis, 14 scientific publications were selected that fully meet the topic and objectives of the review. The results obtained allow you better understand the current state of research in this area and can be useful in developing more efficient and secure mechanisms for processing queries with subsequent data analysis.