Privacy-enhancing mechanisms for the scenario of participatory sensing and smart grids

Abstract

In this talk I am going to give a brief overview of my PhD project and then present in detail some work in progress that introduces a common modeling framework for participatory sensing, smart grids and other smart city applications. My PhD work aims at increasing user adoption of technologies for participatory sensing and the smart grid. User adoption depends on different variables, I focus on the issue of privacy and the development of privacy-enhancing mechanisms. The last project of the PhD goes beyond privacy and considers other human-centred measures, such as fairness and social welfare. The goal is to provide a tool to measure the quality of algorithms depending on a set of preferences about trade-offs between measures such as success, efficiency and other human-centred measures. I will introduce a general modeling framework, based on voluntary contribution games, suitable for different smart city applications, such as participatory sensing and smart grids. I will present preliminary results quantifying trade-offs associated with various centralized and distributed algorithms. These results could be interesting for designers and providers of privacy-sensitive services for the Smart City, as they act as a guideline for choosing the appropriate algorithm for a concrete scenario. These findings could also be useful for consumers as a tool to evaluate different services according to their preferences regarding these trade-offs.

Short Bio

Stefano Bennati is a PhD student at the chair of Computational Social Science at the Swiss Federal Institute of Technology (ETH) Zürich under the supervision of Prof. Dirk Helbing. He has been a research assistant at University of Freiburg and Carnegie Melon University, as well as a visiting research student at TU Delft and Leeds University. His research focuses on the effects of learning on social interactions, and on turning these effects into technology that can improve social aspects such as privacy, discrimination and inequality. He takes a multi-disciplinary approach at the intersection between the social sciences and computer science, with his methodology focusing on three pillars: computer simulations, machine learning and data analysis. 

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