PPNNBP: A Third Party Privacy-Preserving Neural Network With Back-Propagation Learning
IEEE Access
With the advances in machine learning techniques and the potency of cloud computing there is an increasing adoption of third party cloud services for outsourcing training and prediction of machine learning models. Although cloud-hosted machine learning services enable more efficient storage and computation of data, privacy concerns and data sovereignty issues remain a major challenge. Privacy-preserving machine learning provides a promising solution. In this paper, a privacy-preserving neural network generation and utilization framework is presented, the PPNNBP framework. PPNNBP allows model training and prediction to be securely delegated to a third party with minimal data owner participation once the input data have been encrypted without recourse to secret sharing or multiple party setting. This is achieved using a proposed fully homomorphic encryption scheme, the Modified Liu Scheme (MLS …
The paper introduces the Secure kNN (SkNN) approach to data classification and querying. The approach is founded on the concept of Secure Chain Distance Matrices (SCDMs) whereby the classification…
With the advances in machine learning techniques and the potency of cloud computing there is an increasing adoption of third party cloud services for outsourcing training and prediction of machine…
To study the variation in emotional responses to stimuli, different methods have been developed to elicit emotions in a replicable way. Using video clips has been shown to be the most effective…