Keywords: Participatory Sensing , Smart Cities , Public Good , Privacy , Fairness

This paper introduces a scenario-independent design principle for developing smart city algorithms, based on the theory of public goods and voluntary contribution games.
Voluntary contributions are well suited for modeling scenarios where a common resource, i.e. a smart-city service, depends on the users contributing resources, e.g. user-generated data, energy surplus.
The choices of users are modeled by contribution strategies, algorithms that determine which users should contribute to the system at each point in time, based on the state of the system and a set of system requirements.
By acting on the decision of whether to participate, a contribution strategy is independent of the characteristics of the resource, hence it can be combined with existing mechanism that work at the content level, e.g., privacy-preserving algorithms.
A simulation framework is developed that allows a comparison of multiple algorithms for resource contribution on the application scenarios of traffic congestion and charging of electric vehicles, to address the research question: how do different contribution strategies compare in terms of privacy, fairness and social welfare?
Results show that trade-offs between measures and algorithms repeat across the application scenarios, hence this work can be of interest to designers of smart city applications and services that look for a guideline for the choice of algorithm, given specific scenario priorities in terms of different measures.

Keywords: privacy, Internet of Things, Smart City, network, sensor, grouping, agent, aggregation

Big data collection practices using Internet of Things (IoT) pervasive technologies are often
privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices
open up tremendous opportunities for managing and regulating smart city infrastructures in a more efficient and sustainable way. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus and challenge tackled in this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared to a baseline scenario in which each individual shares data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system and its generic applicability are evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed.

Keywords: privacy, machine learning, distributed sensor networks, event detection

This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
method for reducing the privacy cost of information transmitted through a network. Distributed sensor networks are often used for automated classification and
detection of abnormal events in high-stakes situations, e.g. fire in buildings, earthquakes, or crowd disasters. Such networks might transmit privacy-sensitive information, e.g. GPS location of smartphones, which might be disclosed if the network
is compromised. Privacy concerns might slow down the adoption of the technology, in particular in the scenario of social sensing where participation is voluntary,
thus solutions are needed which improve privacy without compromising on the event
detection accuracy.
PriMaL is implemented as a machine-learning layer that works on top of an
existing event detection algorithm. Experiments are run in a general simulation
framework, for several network topologies and parameter values. The privacy footprint of state-of-the-art event detection algorithms is compared within the proposed
framework. Results show that PriMaL is able to reduce the privacy cost of a distributed event detection algorithm below that of the corresponding centralized algorithm, within the bounds of some assumptions about the protocol. Moreover the
performance of the distributed algorithm is not statistically worse than that of the
centralized algorithm.