| Title | Secure Outsourcing of Fuzzy Linear Regression in Cloud Computing |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Gisin, Vladimir B., Volkova, Elena S. |
| Conference Name | 2021 XXIV International Conference on Soft Computing and Measurements (SCM) |
| Date Published | may |
| Keywords | cloud computing, Computing Theory, fuzzy linear regression, Linear programming, linear regression, machine learning, Metrics, outsourcing, Protocols, pubcrawl, Secure computing, security, Tools, transformational approach |
| Abstract | There are problems in which the use of linear regression is not sufficiently justified. In these cases, fuzzy linear regression can be used as a modeling tool. The problem of constructing a fuzzy linear regression can usually be reduced to a linear programming problem. One of the features of the resulting linear programming problem is that it uses a relatively large number of constraints in the form of inequalities with a relatively small number of variables. It is known that the problem of constructing a fuzzy linear regression is reduced to the problem of linear programming. If the user does not have enough computing power the resulting problem can be transferred to the cloud server. Two approaches are used for the confidential transfer of the problem to the server: the approach based on cryptographic encryption, and the transformational approach. The paper describes a protocol based on the transformational approach that allows for secure outsourcing of fuzzy linear regression. |
| DOI | 10.1109/SCM52931.2021.9507102 |
| Citation Key | gisin_secure_2021 |
Secure Outsourcing of Fuzzy Linear Regression in Cloud Computing