Abstract:
We introduce an overview of modern approaches to cloud confidential data processing. A significant part of data warehouse and data processing systems is based on cloud services. Users and organizations consider such services as a service provider. This approach allows users to take benefit from all of these technologies: they do not need to purchase, install and maintain expensive equipment, they can access the data and the calculation results from any device. Such data processing on cloud services carries certain risks because one of the participants of the protocol for securing access to cloud data storage may be an adversary. This leads to the threat of confidential information leakage. The above approaches are intended for databases in which information is stored in the encrypted form and they allow to work in the familiar paradigm of SQL queries. Despite the advantages such approach has some limitations. It is necessary to choose an encryption method and to maintain a balance between the reliability of encryption and the set of requests required by users. In the case if users are not limited by the framework of SQL queries, we propose another way of implementation of cloud computing over confidential data using free software. It is based on lambda architecture combined with certain restrictions on allowed deductively safe database queries.
Citation:
S. A. Martishin, M. V. Khrapchenko, A. V. Shokurov, “Study of the problem of ensuring security in the storage and processing of confidential data”, Proceedings of ISP RAS, 33:2 (2021), 173–190
\Bibitem{MarKhrSok21}
\by S.~A.~Martishin, M.~V.~Khrapchenko, A.~V.~Shokurov
\paper Study of the problem of ensuring security in the storage and processing of confidential data
\jour Proceedings of ISP RAS
\yr 2021
\vol 33
\issue 2
\pages 173--190
\mathnet{http://mi.mathnet.ru/tisp593}
\crossref{https://doi.org/10.15514/ISPRAS-2021-33(2)-11}
Linking options:
https://www.mathnet.ru/eng/tisp593
https://www.mathnet.ru/eng/tisp/v33/i2/p173
This publication is cited in the following 1 articles:
Abduani Abduklimu, Peng Yan, Gang Wang, Hu Liu, Junpeng Xie, 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), 2023, 780