Very good news! Our paper “Understanding the Data-Processing Challenges in Intelligent Vehicular Systems” has been accepted at the IEEE Intelligent Vehicles Symposium (IV 2016).

The abstract follows:
Vehicular sensors able to perceive and measure the environment, ranging from in-vehicle sensors to speed cameras, are revolutionizing how technology can interact with our daily lives, enabling Intelligent Vehicular Systems (IVSs). These sensors generate large volumes of data which can reveal useful information for enhancing the sustainable development (through improved utilization of resources), as well as the safety and functionality of the system.
In this context, a key challenge is to reduce the large data streams into manageable sets of valuable information in a real-time, reliable and cost-affordable fashion. Due to the data volume size and velocity, relying exclusively on traditional data processing systems, such as databases and batch processing, is no longer a suitable option, since it is not feasible to store the data to later process it. Moreover, careful decisions should be made to leverage the existing computing capacity, from embedded devices found in the IVSs to cloud infrastructures.
In this paper we study trade-offs of possible options for data-stream processing models and computing infrastructures. Through building an experimental platform that emulates realistic components of a future deployable IVS and validating two different data-stream processing systems with a well-known benchmark for IVSs, we study options and trade-offs in real-time data stream processing in IVS infrastructures. Our evaluation shows that existing data-stream processing models can be leveraged in different ways, based on the processing requirements.

Very good news! Our paper “BES – Differentially Private and Distributed Event Aggregation in Advanced Metering Infrastructures.” has been accepted at the 2nd ACM Cyber-Physical System Security Workshop (CPSS 2016), held in conjunction with ACM AsiaCCS’16.

The abstract follows:
Significant challenges for online event aggregation in the context of Cyber-Physical Systems stem from the computational requirements of their distributed nature, as well as from their privacy concerns. In the context of the latter, differential privacy has gained popularity because of its strong privacy protection guarantees, holding against very powerful adversaries. Despite such strong guarantees, though, its adoption in real-world applications is limited by the privacy-preserving noise it introduces to the analysis, which might compromise its usefulness.
We investigate the above problem from a system-perspective in the context of Advanced Metering Infrastructures, providing strong privacy guarantees together with useful results for event aggregation taking into account the distributed nature of such systems. We present a streaming-based framework, Bes, and propose methods to limit the noise introduced by differential privacy in real-world scenarios, thus reducing the resulting utility degradation, while still holding against the adversary model adhering with the original definition of differential privacy.
We provide a thorough evaluation based on a fully implemented Bes prototype and conducted with real energy consumption data. We show how a large number of events can be aggregated in a private fashion with low processing latency by a single-board device, similar in performance to the devices deployed in Advanced Metering Infrastructures.

We currently have open positions for both Ph.D. and Postdoctoral students. Please share! (and apply, if you are interested!)

You can find information about Ph.D. positions here while you can find information about Postdoctoral positions here.