@Article{jcs.2020.09308,
AUTHOR = {Leqi Jiang, Zhangjie Fu},
TITLE = {Privacy-Preserving Genetic Algorithm Outsourcing in Cloud  Computing},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {49--61},
URL = {http://www.techscience.com/JCS/v2n1/39372},
ISSN = {2579-0064},
ABSTRACT = {Genetic Algorithm (GA) has been widely used to solve various optimization 
problems. As the solving process of GA requires large storage and computing resources, 
it is well motivated to outsource the solving process of GA to the cloud server. However, 
the algorithm user would never want his data to be disclosed to cloud server. Thus, it is 
necessary for the user to encrypt the data before transmitting them to the server. But the 
user will encounter a new problem. The arithmetic operations we are familiar with cannot 
work directly in the ciphertext domain. In this paper, a privacy-preserving outsourced 
genetic algorithm is proposed. The user’s data are protected by homomorphic encryption 
algorithm which can support the operations in the encrypted domain. GA is elaborately 
adapted to search the optimal result over the encrypted data. The security analysis and 
experiment results demonstrate the effectiveness of the proposed scheme.},
DOI = {10.32604/jcs.2020.09308}
}