As pervasive computing spreads, the physical reality and its phenomena are mapped into the digital world, generating large amount of data that can be interpreted and correlated to a variety of personal information. Its development has incorporated the advances in sensor, networking, web service , and data processing technologies, creating a new era of connected things, so called the Internet of Things (IoT). The IoT sensing service, scattered by the network – Virtual Sensor Network (VSN) –, has been leveraged by the Cloud Computing, delivering scalable, virtualized and geographical proximity resources. In this paper, we present an experiment on deploying Sensor-Clouds that provides a privacy-aware Sensing as a Service (pSaaS) using a new privacy model. This model provides twofold Privacy Enforcement Point (PEP) that intermediates connected data providers and data consumer, implementing an in-network verification process that reasons about inference intention and personal information in order to deny access or degrade data utility to specific parts of the IoT data stream. We investigate how this novel pSaaS paradigm can be implemented to provide on-demand sensing of meaningful personal information through in-network data processing while still enforcing privacy. In addition, we investigate how to support its on-demand deployment on the Sensor-Cloud using a real VSN middleware.