Participatory sensing takes advantage of the pervasive nature of mobile phones to collect data about the urban environment using the available sensors. Campaignr makes collecting this data as simple as a few button pushes. It provides access to the sensors in a robust and flexible way that hides the complexities of the mobile embedded phone environment. This paper describes the design choices and provides some numerical evaluation of Campaignr. Campaignr has been and is being actively used as the data collection method for many research pro jects, both internally and externally.

Campaignr is a software architecture for cellphones that enables owners of smartphones (specifically Symbian S60 3rd Edition phones) to participate in data gathering campaigns. Campaigns are set up by individuals or groups of people that are interested in exploring a specific piece of the urban environment. Campaignr can upload the data collected by the cellphone to any online storage place that supports XML (or JSON) or can store the information on the phone's memory for later retrieval. It is designed to work without connection to support data collection in areas of poor to no connectivity so that the cellphones can be used in places without any network coverage for more rural or even wilderness applications. Campaignr provides easy access to the hardware sensors such as camera, microphone, cell tower information, and GPS (whether internal or external). It also can access metadata that provides relevant and useful information, such as the current time of the phone, the globally unique serial number, and user generated text input. Campaigns are defined by an XML file that contains options on which sensors to collect, how to collect data from those sensors, where to put the collected data, and how the user interface behaves.

The use of XML files allows Campaignr to be highly configurable without having to change the underlying code. No recompiling, testing, debugging, or reinstalling is needed. This is useful because developing applications or the embedded smartphone environment is significantly more challenging than for the desktop.

It has become cliché to observe that new information technologies endanger privacy. Typically, the threat is viewed as coming from Big Brother (the government) or Company Man (the firm). But for a nascent data practice we call “self-surveillance,” the threat may actually come from ourselves. Using various existing and emerging technologies, such as GPS-enabled smartphones, we are beginning to measure ourselves in granular detail – how long we sleep, where we drive, what we breathe, what we eat, how we spend our time. And we are storing these data casually, perhaps promiscuously, somewhere in the “cloud,” and giving third-parties broad access. This data practice of self-surveillance will decrease information privacy in troubling ways. To counter this trend, we recommend the creation of the Privacy Data Guardian, a new profession that manages Privacy Data Vaults, which are repositories for self-surveillance data. In addition to providing technical specifications of this approach, we outline the specific legal relations, which include a fiduciary relationship, between client and Guardian. In addition, we recommend the creation of an evidentiary privilege, similar to a trade secret privilege, that protects self-surveillance data held by a licensed Guardian. We also answers objections that our solution is implausible or useless. We conclude by pointing out that various legal, technological, and self-regulatory attempts at safeguarding privacy from new digital, interconnected technologies have not been particularly successful. Before self-surveillance becomes a widespread practice, some new innovation is needed. In our view, that innovation is a new “species,” the Personal Data Guardian, created through a fusion of law and technology and released into the current information ecosystem.

Urban sensing systems that use mobile phones enable individuals and communities to collect and share data with unprecedented speed, accuracy and granularity. But employing mobile handsets as sensor nodes poses new challenges for privacy, data security, and ethics. To address these challenges, CENS is developing design principles based upon understanding privacy regulation as a participatory process. This paper briefly reviews related literature and introduces the concept of participatory privacy regulation. PPR reframes negotiations of social context as an important part of participation in sensing-supported research. It engages participants in ethical decision-making and the meaningful negotiation of personal boundaries and identities. We use PPR to establish a set of design principles based on our application drivers.

We are deploying a metropolitan scale Wi-Fi mesh network near downtown Los Angeles to support the design and development of a data-centric network-fabric for urban participatory sensing. Participatory sensing employs software and network technology to enable people’s everyday mobile devices to act as credible sensors of the natural, built, and cultural environments. Current research focuses on how to make it easy and secure for both the public and professional users to define sensing ‘campaigns,’ recruit participants to collect data, to help ‘make a case’ with data they collect, and digitally publish the results. To further research in this area, our architecture will enable embedding network–attested location and time context in sensor readings. The network will also provide a research framework for developing policy-based privacy, and related security mechanisms for participatory sensing.

This paper introduces the concept of participatory sensing, which tasks everyday mobile devices, such as cellular phones, to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. An initial architecture to enhance data credibility, quality, privacy and ‘shareability’ in such networks is described, as well as a campaign application model that encompasses participation at personal, social and urban scales. Example applications are outlined in four areas: urban planning, public health, cultural identity and creative expression, and natural resource management.

Human memory is a selective process where details are often lost, yet some types of research relies heavily on human memorization. Studies have shown that photographs help enhance the memory significantly. This study envisions to create a low-cost, mass-scale system using cell phones and to handle the massive amount of data that live image capture tends to implicate.

For decades, the Codes of Fair Information Practice have served as a model for data privacy, protecting personal information collected by governments and corporations. But professional data management standards such as the Codes of Fair Information Practice do not take into account a world of distributed data collection, nor the realities of data mining and easy, almost uncontrolled, dissemination. Emerging models of information gathering create an environment where recording devices, deployed by individuals rather than organizations, disrupt expected flows of information in both public and private spaces. We suggest expanding the Codes of Fair Information Practice to protect privacy in this new data reality. An adapted understanding of the Codes of Fair Information Practice can promote individuals’ engagement with their own data, and apply not only to governments and corporations, but software developers creating the data collection programs of the 21st century. To support user participation in regulating sharing and disclosure, we discuss three foundational design principles: primacy of participants, data legibility, and engagement of participants throughout the data life cycle. We also discuss social changes that will need to accompany these design principles, including engagement of groups and appeal to the public sphere, increasing transparency of services through voluntary or regulated labeling, and securing a legal privilege for raw location data.