Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage
| Autor: | Vanessa Miranda-Lopez, Andrei Tchernykh, Mikhail Babenko, Alexander Yu. Drozdov, Gleb Radchenko, Fermin-Alberto Armenta-Cano, Arutyun Avetisyan |
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| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
Computer science Group method of data handling business.industry 020206 networking & telecommunications Worst-case scenario 02 engineering and technology Secret sharing Data recovery 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Error detection and correction business Cloud storage Algorithm Software Decoding methods |
| Zdroj: | Cluster Computing. 22:1173-1185 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-018-02896-9 |
| Popis: | Properties of redundant residue number system (RRNS) are used for detecting and correcting errors during the data storing, processing and transmission. However, detection and correction of a single error require significant decoding time due to the iterative calculations needed to locate the error. In this paper, we provide a performance evaluation of Asmuth-Bloom and Mignotte secret sharing schemes with three different mechanisms for error detecting and correcting: Projection, Syndrome, and AR-RRNS. We consider the best scenario when no error occurs and worst-case scenario, when error detection needs the longest time. When examining the overall coding/decoding performance based on real data, we show that AR-RRNS method outperforms Projection and Syndrome by 68% and 52% in the worst-case scenario. |
| Databáze: | OpenAIRE |
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