4 accepted papers
In this paper, we propose access control for privacy-preserving Gaussian process regression (GPR), in which the encrypted data are generated through a random unitary transform (RUT). The proposed secure GPR enables computation in both encrypted input and output domains, and the access to inputs and…
In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation performance as the Label Consistent K-Singular Va…
Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service, which, in turn, has raised significant concerns on privacy as the aggregated informa…
Big-data analysis by edge/cloud systems is becoming more important. However, when information may lead to personal identification, such information tends to be encrypted and restricted to its owners to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful an…