n the last decade, researchers have envisioned an outbreak of wireless sensor networks (WSNs) and predicted the widespread installation of sensor (e.g., in infrastructures, buildings, woods, rivers, or even the atmosphere). This has triggered a lot of interest in many different WSN topics, including identifying and addressing security issues, such as data integrity, node capture, and secure routing. On the contrary, privacy has not really been a concern in WSNs, as sensors are usually owned, operated, and queried by the same entity. (For instance, the National Department of Transportation deploys sensors and collects traffic information related to national highways.) On the other hand, the proliferation of mobile phones, along with their pervasive connectivity, has propelled the amount of digital data produced and processed every day. This has driven researchers and IT professionals to discuss and develop a novel sensing paradigm, where sensors are not deployed in specific locations, but are carried around by people. Today, many different sensors are already deployed in our mobile phones, and soon all our gadgets (e.g., even our clothes or cars) will embed a multitude of sensors (GPS, digital imagers, accelerometers, etc.). As a result, data collected by sensor-equipped devices becomes of extreme interest to other users and applications. For instance, mobile phones may report (in real time) temperature or noise level; similarly, cars may inform on traffic conditions. This paradigm is called participatory sensing (PS) -sometimes also referred to as opportunistic or urban sensing [3] . It combines the ubiquity of personal devices with sensing capabilities typical of WSNs. As the number of mobile phone subscriptions exceeds 5 billion, PS becomes a cutting-edge and effective distributed computing (as well as business) model. We argue that PS appreciably expands the capabilities of WSN applications by, for example, allowing effective monitor-ing in scenarios where the setup of a WSN is either not economical or infeasible. However, its success is strongly related to the number of users actually willing to commit personal device resources to sensing applications, and thus to associated privacy concerns. Observe that sensing devices are no longer "dull" gadgets owned by the entity querying them. They are personal devices that follow users at all times, and their reports often expose personal and sensitive information. Consider, for instance, a PS application like http://www.gasbuddy.com/ where gas prices are monitored via user reports, and information announced by participants inevitably exposes their current and past locations, and hence their movements. If users have no incentive to contribute sensed data or feel that their privacy might be violated, they will (most likely) refuse to participate. Thus, not only traditional security but also privacy issues must be taken into account. In this article, we focus on privacy protection in PS. We define privacy in this new context, present a privacyenhanced PS infrastructure, and elaborate on a number of desirable features that constitute challenging research problems. The proposed privacy-protecting layer can easily be adopted by available PS applications to enforce privacy and enhance user participation. What Is Participatory Sensing? PS is an emerging paradigm that focuses on the seamless collection of information from a large number of connected, always on, always carried devices, such as mobile phones. PS leverages the wide proliferation of commodity sensorequipped devices and the ubiquity of broadband network infrastructure to provide sensing applications where deployment of a WSN infrastructure is not economical or infeasible. Participatory sensing is an emerging computing paradigm that enables the distributed collection of data by self-selected participants. It allows the increasing number of mobile phone users to share local knowledge acquired by their sensor-equipped devices (e.g., to monitor temperature, pollution level, or consumer pricing information). While research initiatives and prototypes proliferate, their real-world impact is often bounded to comprehensive user participation. If users have no incentive, or feel that their privacy might be endangered, it is likely that they will not participate. In this article, we focus on privacy protection in participatory sensing and introduce a suitable privacy-enhanced infrastructure. First, we provide a set of definitions of privacy requirements for both data producers (i.e., users providing sensed information) and consumers (i.e., applications accessing the data). Then we propose an efficient solution designed for mobile phone users, which incurs very low overhead. Finally, we discuss a number of open problems and possible research directions.
De Cristofaro et al. REF proposed a privacy-aware infrastructure for building participatory sensing applications to protect the data of both the data user and the provider.
Participatory privacy: Enabling privacy in participatory sensing
IEEE Network
IEEE Network
Computer Science