Holographic Internet of Things (IoT) aggregates virtual and augmented reality to provide real-time modeling that improves the user experience of consumer electronic products and applications. The incorporated technologies support third-party applications for which heterogeneous privacy-preserving features are required. Considering this factor, a Modeling Space Privacy (MSP) is introduced in this article using ternary homomorphic encryption (THE). The proposed privacy scheme encourages space and component privacy using independent hashes using HE. Privacy is retained using the ternary operation between the components and space to ensure maximum security of IoT model representations. Third-party applications, components, and spaces need to follow a unison between the IoT platforms to improve security. The components/space elements are discarded from the holographic representations to prevent anonymous access/views to the actual models. The federated learning process in the proposed scheme differs from the ternary process. Therefore, the proposed scheme is reliable in employing conditional HE to improve the privacy of holographic IoT platforms irrespective of multi-party inclusions. From the comparative values, the proposed scheme is optimal in reducing the latency by 13.412% and privacy process complexity by 12.664% for the maximum devices considered.

Pepper is a humanoid robot capable of expressing body language, perceiving, and interacting with its surrounding environment, thanks to a wide set of sensors and actuators and exposing capabilities and high-level interfaces for natural interaction with humans. In this article, we present the development of VPepper, the Pepper virtual replica, by describing experiences focused on the interaction of the digital twin with the replicas of the smart objects in a smart home. Pepper robot has been featured with arms and hands, but its motors and actuators cannot support intensive experimental sessions and training procedures to learn how safely touch objects. Here, digital twin metaphor plays a crucial role. By a virtual and reliable replica of the robot, machine learning procedures can be seamlessly moved to/from the digital twin with a significant speedup and preventing the physical robot from deterioration. As a practical application, the reported case study is inspired to ambient-assisted living in elderly assistance. The experience, as well as the entire design and development process, has revealed VPepper and the smart environment to offer interesting opportunities for the physical accuracy of the simulation and for the availability of machine learning instruments that may be converted and adopted for real settings. A final empirical evaluation, performed involving 25 volunteer caregivers, confirms the perceived value and the potential usefulness of the system.