A retrievable data perturbation method used in privacy-preserving in cloud computing

With the increasing popularity of cloud computing, privacy has become one of the key problem in cloud security. When data is outsourced to the cloud, for data owners, they need to ensure the security of their privacy; for cloud service providers, they need some information of the data to provide high QoS services; and for authorized users, they need to access to the true value of data. The existing privacy-preserving methods can't meet all the needs of the three parties at the same time. To address this issue, we propose a retrievable data perturbation method and use it in the privacy-preserving in data outsourcing in cloud computing. Our scheme comes in four steps. Firstly, an improved random generator is proposed to generate an accurate ¿noise¿. Next, a perturbation algorithm is introduced to add noise to the original data. By doing this, the privacy information is hidden, but the mean and covariance of data which the service providers may need remain unchanged. Then, a retrieval algorithm is proposed to get the original data back from the perturbed data. Finally, we combine the retrievable perturbation with the access control process to ensure only the authorized users can retrieve the original data. The experiments show that our scheme perturbs date correctly, efficiently, and securely.