Privacy-Invading Technologies and Privacy by Design

Curated on Posted on by Stefaan Verhulst

New book by Demetrius Klitou: “Challenged by rapidly developing privacy-invading technologies (PITs), this book provides a convincing set of potential policy recommendations and practical solutions for safeguarding both privacy and security. It shows that benefits such as public security do not necessarily come at the expense of privacy and liberty overall.
Backed up by comprehensive study of four specific PITs – Body scanners; Public space CCTV microphones; Public space CCTV loudspeakers; and Human-implantable microchips (RFID implants/GPS implants) – the author shows how laws that regulate the design and development of PITs may more effectively protect privacy than laws that only regulate data controllers and the use of such technologies. New rules and regulations should therefore incorporate fundamental privacy principles through what is known as ‘Privacy by Design’.
The numerous sources explored by the author provide a workable overview of the positions of academia, industry, government and relevant international organizations and NGOs.

  • Explores a relatively novel approach of protecting privacy
  • Offers a convincing set of potential policy recommendations and practical solutions
  • Provides a workable overview of the positions of academia, industry, government and relevant international organizations and NGOs”

No silver bullet: De-identification still doesn’t work

Curated on Posted on by Stefaan Verhulst

Arvind Narayanan and Edward W. Felten: “Paul Ohm’s 2009 article Broken Promises of Privacy spurred a debate in legal and policy circles on the appropriate response to computer science research on re-identification techniques. In this debate, the empirical research has often been misunderstood or misrepresented. A new report by Ann Cavoukian and Daniel Castro is full of such inaccuracies, despite its claims of “setting the record straight.” In a response to this piece, Ed Felten and I point out eight of our most serious points of disagreement with Cavoukian and Castro. The thrust of our arguments is that (i) there is no evidence that de-identification works either in theory or in practice and (ii) attempts to quantify its efficacy are unscientific and promote a false sense of security by assuming unrealistic, artificially constrained models of what an adversary might do. Specifically, we argue that:

  1. There is no known effective method to anonymize location data, and no evidence that it’s meaningfully achievable.
  2. Computing re-identification probabilities based on proof-of-concept demonstrations is silly.
  3. Cavoukian and Castro ignore many realistic threats by focusing narrowly on a particular model of re-identification.
  4. Cavoukian and Castro concede that de-identification is inadequate for high-dimensional data. But nowadays most interesting datasets are high-dimensional.
  5. Penetrate-and-patch is not an option.
  6. Computer science knowledge is relevant and highly available.
  7. Cavoukian and Castro apply different standards to big data and re-identification techniques.
  8. Quantification of re-identification probabilities, which permeates Cavoukian and Castro’s arguments, is a fundamentally meaningless exercise.

Data privacy is a hard problem. Data custodians face a choice between roughly three alternatives: sticking with the old habit of de-identification and hoping for the best; turning to emerging technologies like differential privacy that involve some trade-offs in utility and convenience; and using legal agreements to limit the flow and use of sensitive data. These solutions aren’t fully satisfactory, either individually or in combination, nor is any one approach the best in all circumstances. Change is difficult. When faced with the challenge of fostering data science while preventing privacy risks, the urge to preserve the status quo is understandable. However, this is incompatible with the reality of re-identification science. If a “best of both worlds” solution exists, de-identification is certainly not that solution. Instead of looking for a silver bullet, policy makers must confront hard choices.”

Urban Analytics (Updated and Expanded)

Curated on Posted on by Andrew Young

As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Urban Analytics. To suggest additional readings on this or any other topic, please email biblio@thegovlab.org.

Data and its uses for Governance

Urban Analytics places better information in the hands of citizens as well as government officials to empower people to make more informed choices. Today, we are able to gather real-time information about traffic, pollution, noise, and environmental and safety conditions by culling data from a range of tools: from the low-cost sensors in mobile phones to more robust monitoring tools installed in our environment. With data collected and combined from the built, natural and human environments, we can develop more robust predictive models and use those models to make policy smarter.

With the computing power to transmit and store the data from these sensors, and the tools to translate raw data into meaningful visualizations, we can identify problems as they happen, design new strategies for city management, and target the application of scarce resources where they are most needed.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)
Amini, L., E. Bouillet, F. Calabrese, L. Gasparini, and O. Verscheure. “Challenges and Results in City-scale Sensing.” In IEEE Sensors, 59–61, 2011. http://bit.ly/1doodZm.

  • This paper examines “how city requirements map to research challenges in machine learning, optimization, control, visualization, and semantic analysis.”
  • The authors raises several research challenges including how to extract accurate information when the data is noisy and sparse; how to represent findings from digital pervasive technologies; and how people interact with one another and their environment.

Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis, and Y. Portugali. “Smart Cities of the Future.” The European Physical Journal Special Topics 214, no. 1 (November 1, 2012): 481–518. http://bit.ly/HefbjZ.

  • This paper explores the goals and research challenges involved in the development of smart cities that merge ICT with traditional infrastructures through digital technologies.
  • The authors put forth several research objectives, including: 1) to explore the notion of the city as a laboratory for innovation; 2) to develop technologies that ensure equity, fairness and realize a better quality of city life; and 3) to develop technologies that ensure informed participation and create shared knowledge for democratic city governance.
  • The paper also examines several contemporary smart city initiatives, expected paradigm shifts in the field, benefits, risks and impacts.

Budde, Paul. “Smart Cities of Tomorrow.” In Cities for Smart Environmental and Energy Futures, edited by Stamatina Th Rassia and Panos M. Pardalos, 9–20. Energy Systems. Springer Berlin Heidelberg, 2014. http://bit.ly/17MqPZW.

  • This paper examines the components and strategies involved in the creation of smart cities featuring “cohesive and open telecommunication and software architecture.”
  • In their study of smart cities, the authors examine smart and renewable energy; next-generation networks; smart buildings; smart transport; and smart government.
  • They conclude that for the development of smart cities, information and communication technology (ICT) is needed to build more horizontal collaborative structures, useful data must be analyzed in real time and people and/or machines must be able to make instant decisions related to social and urban life.

Cardone, G., L. Foschini, P. Bellavista, A. Corradi, C. Borcea, M. Talasila, and R. Curtmola. “Fostering Participaction in Smart Cities: a Geo-social Crowdsensing Platform.” IEEE Communications
Magazine 51, no. 6 (2013): 112–119. http://bit.ly/17iJ0vZ.

  • This article examines “how and to what extent the power of collective although imprecise intelligence can be employed in smart cities.”
  • To tackle problems of managing the crowdsensing process, this article proposes a “crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.”

Chen, Chien-Chu. “The Trend towards ‘Smart Cities.’” International Journal of Automation and Smart Technology. June 1, 2014. http://bit.ly/1jOOaAg.

  • In this study, Chen explores the ambitions, prevalence and outcomes of a variety of smart cities, organized into five categories:
    • Transportation-focused smart cities
    • Energy-focused smart cities
    • Building-focused smart cities
    • Water-resources-focused smart cities
    • Governance-focused smart cities
  • The study finds that the “Asia Pacific region accounts for the largest share of all smart city development plans worldwide, with 51% of the global total. Smart city development plans in the Asia Pacific region tend to be energy-focused smart city initiatives, aimed at easing the pressure on energy resources that will be caused by continuing rapid urbanization in the future.”
  • North America, on the other hand is generally more geared toward energy-focused smart city development plans. “In North America, there has been a major drive to introduce smart meters and smart electric power grids, integrating the electric power sector with information and communications technology (ICT) and replacing obsolete electric power infrastructure, so as to make cities’ electric power systems more reliable (which in turn can help to boost private-sector investment, stimulate the growth of the ‘green energy’ industry, and create more job opportunities).”
  • Looking to Taiwan as an example, Chen argues that, “Cities in different parts of the world face different problems and challenges when it comes to urban development, making it necessary to utilize technology applications from different fields to solve the unique problems that each individual city has to overcome; the emphasis here is on the development of customized solutions for smart city development.”

Domingo, A., B. Bellalta, M. Palacin, M. Oliver and E. Almirall. “Public Open Sensor Data: Revolutionizing Smart Cities.” Technology and Society Magazine, IEEE 32, No. 4. Winter 2013. http://bit.ly/1iH6ekU.

  • In this article, the authors explore the “enormous amount of information collected by sensor devices” that allows for “the automation of several real-time services to improve city management by using intelligent traffic-light patterns during rush hour, reducing water consumption in parks, or efficiently routing garbage collection trucks throughout the city.”
  • They argue that, “To achieve the goal of sharing and open data to the public, some technical expertise on the part of citizens will be required. A real environment – or platform – will be needed to achieve this goal.” They go on to introduce a variety of “technical challenges and considerations involved in building an Open Sensor Data platform,” including:
    • Scalability
    • Reliability
    • Low latency
    • Standardized formats
    • Standardized connectivity
  • The authors conclude that, despite incredible advancements in urban analytics and open sensing in recent years, “Today, we can only imagine the revolution in Open Data as an introduction to a real-time world mashup with temperature, humidity, CO2 emission, transport, tourism attractions, events, water and gas consumption, politics decisions, emergencies, etc., and all of this interacting with us to help improve the future decisions we make in our public and private lives.”

Harrison, C., B. Eckman, R. Hamilton, P. Hartswick, J. Kalagnanam, J. Paraszczak, and P. Williams. “Foundations for Smarter Cities.” IBM Journal of Research and Development 54, no. 4 (2010): 1–16. http://bit.ly/1iha6CR.

  • This paper describes the information technology (IT) foundation and principles for Smarter Cities.
  • The authors introduce three foundational concepts of smarter cities: instrumented, interconnected and intelligent.
  • They also describe some of the major needs of contemporary cities, and concludes that Creating the Smarter City implies capturing and accelerating flows of information both vertically and horizontally.

Hernández-Muñoz, José M., Jesús Bernat Vercher, Luis Muñoz, José A. Galache, Mirko Presser, Luis A. Hernández Gómez, and Jan Pettersson. “Smart Cities at the Forefront of the Future Internet.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 447–462. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/HhNbMX.

  • This paper explores how the “Internet of Things (IoT) and Internet of Services (IoS), can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform.”
  • The authors examine the SmartSantander project to argue that, “the different stakeholders involved in the smart city business is so big that many non-technical constraints must be considered (users, public administrations, vendors, etc.).”
  • The authors also discuss the need for infrastructures at the, for instance, European level for realistic large-scale experimentally-driven research.

Hoon-Lee, Jung, Marguerite Gong Hancock, Mei-Chih Hu. “Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco.” Technological Forecasting and Social Change. Ocotober 3, 2013. http://bit.ly/1rzID5v.

  • In this study, the authors aim to “shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature.”
  • They propose a conceptual framework based on case studies from Seoul and San Francisco built around the following dimensions:
    • Urban openness
    • Service innovation
    • Partnerships formation
    • Urban proactiveness
    • Smart city infrastructure integration
    • Smart city governance
  • The authors conclude with a summary of research findings featuring “8 stylized facts”:
    • Movement towards more interactive services engaging citizens;
    • Open data movement facilitates open innovation;
    • Diversifying service development: exploit or explore?
    • How to accelerate adoption: top-down public driven vs. bottom-up market driven partnerships;
    • Advanced intelligent technology supports new value-added smart city services;
    • Smart city services combined with robust incentive systems empower engagement;
    • Multiple device & network accessibility can create network effects for smart city services;
    • Centralized leadership implementing a comprehensive strategy boosts smart initiatives.

Kamel Boulos, Maged N. and Najeeb M. Al-Shorbaji. “On the Internet of Things, smart cities and the WHO Healthy Cities.” International Journal of Health Geographics 13, No. 10. 2014. http://bit.ly/Tkt9GA.

  • In this article, the authors give a “brief overview of the Internet of Things (IoT) for cities, offering examples of IoT-powered 21st century smart cities, including the experience of the Spanish city of Barcelona in implementing its own IoT-driven services to improve the quality of life of its people through measures that promote an eco-friendly, sustainable environment.”
  • The authors argue that one of the central needs for harnessing the power of the IoT and urban analytics is for cities to “involve and engage its stakeholders from a very early stage (city officials at all levels, as well as citizens), and to secure their support by raising awareness and educating them about smart city technologies, the associated benefits, and the likely challenges that will need to be overcome (such as privacy issues).”
  • They conclude that, “The Internet of Things is rapidly gaining a central place as key enabler of the smarter cities of today and the future. Such cities also stand better chances of becoming healthier cities.”

Keller, Sallie Ann, Steven E. Koonin, and Stephanie Shipp. “Big Data and City Living – What Can It Do for Us?” Significance 9, no. 4 (2012): 4–7. http://bit.ly/166W3NP.

  • This article provides a short introduction to Big Data, its importance, and the ways in which it is transforming cities. After an overview of the social benefits of big data in an urban context, the article examines its challenges, such as privacy concerns and institutional barriers.
  • The authors recommend that new approaches to making data available for research are needed that do not violate the privacy of entities included in the datasets. They believe that balancing privacy and accessibility issues will require new government regulations and incentives.

Kitchin, Rob. “The Real-Time City? Big Data and Smart Urbanism.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, July 3, 2013. http://bit.ly/1aamZj2.

  • This paper focuses on “how cities are being instrumented with digital devices and infrastructure that produce ‘big data’ which enable real-time analysis of city life, new modes of technocratic urban governance, and a re-imagining of cities.”
  • The authors provide “a number of projects that seek to produce a real-time analysis of the city and provides a critical reflection on the implications of big data and smart urbanism.”

Mostashari, A., F. Arnold, M. Maurer, and J. Wade. “Citizens as Sensors: The Cognitive City Paradigm.” In 2011 8th International Conference Expo on Emerging Technologies for a Smarter World (CEWIT), 1–5, 2011. http://bit.ly/1fYe9an.

  • This paper argues that. “implementing sensor networks are a necessary but not sufficient approach to improving urban living.”
  • The authors introduce the concept of the “Cognitive City” – a city that can not only operate more efficiently due to networked architecture, but can also learn to improve its service conditions, by planning, deciding and acting on perceived conditions.
  • Based on this conceptualization of a smart city as a cognitive city, the authors propose “an architectural process approach that allows city decision-makers and service providers to integrate cognition into urban processes.”

Oliver, M., M. Palacin, A. Domingo, and V. Valls. “Sensor Information Fueling Open Data.” In Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, 116–121, 2012. http://bit.ly/HjV4jS.

  • This paper introduces the concept of sensor networks as a key component in the smart cities framework, and shows how real-time data provided by different city network sensors enrich Open Data portals and require a new architecture to deal with massive amounts of continuously flowing information.
  • The authors’ main conclusion is that by providing a framework to build new applications and services using public static and dynamic data that promote innovation, a real-time open sensor network data platform can have several positive effects for citizens.

Perera, Charith, Arkady Zaslavsky, Peter Christen and Dimitrios Georgakopoulos. “Sensing as a service model for smart cities supported by Internet of Things.” Transactions on Emerging Telecommunications Technologies 25, Issue 1. January 2014. http://bit.ly/1qJLDP9.

  • This paper looks into the “enormous pressure towards efficient city management” that has “triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues.”
  • The authors explore the parallel advancement of the Internet of Things (IoT), which “envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities.”
  • The paper proposes the sensing as a service model “as a solution based on IoT infrastructure.” The sensing as a service model consists of four conceptual layers: “(i) sensors and sensor owners; (ii) sensor publishers (SPs); (iii) extended service providers (ESPs); and (iv) sensor data consumers. They go on to describe how this model would work in the areas of waste management, smart agriculture and environmental management.

Privacy, Big Data, and the Public Good: Frameworks for Engagement. Edited by Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum; Cambridge University Press, 2014. http://bit.ly/UoGRca.

  • This book focuses on the legal, practical, and statistical approaches for maximizing the use of massive datasets while minimizing information risk.
  • “Big data” is more than a straightforward change in technology.  It poses deep challenges to our traditions of notice and consent as tools for managing privacy.  Because our new tools of data science can make it all but impossible to guarantee anonymity in the future, the authors question whether it possible to truly give informed consent, when we cannot, by definition, know what the risks are from revealing personal data either for individuals or for society as a whole.
  • Based on their experience building large data collections, authors discuss some of the best practical ways to provide access while protecting confidentiality.  What have we learned about effective engineered controls?  About effective access policies?  About designing data systems that reinforce – rather than counter – access policies?  They also explore the business, legal, and technical standards necessary for a new deal on data.
  • Since the data generating process or the data collection process is not necessarily well understood for big data streams, authors discuss what statistics can tell us about how to make greatest scientific use of this data. They also explore the shortcomings of current disclosure limitation approaches and whether we can quantify the extent of privacy loss.

Schaffers, Hans, Nicos Komninos, Marc Pallot, Brigitte Trousse, Michael Nilsson, and Alvaro Oliveira. “Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 431–446. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/16ytKoT.

  • This paper “explores ‘smart cities’ as environments of open and user-driven innovation for experimenting and validating Future Internet-enabled services.”
  • The authors examine several smart city projects to illustrate the central role of users in defining smart services and the importance of participation. They argue that, “Two different layers of collaboration can be distinguished. The first layer is collaboration within the innovation process. The second layer concerns collaboration at the territorial level, driven by urban and regional development policies aiming at strengthening the urban innovation systems through creating effective conditions for sustainable innovation.”

Suciu, G., A. Vulpe, S. Halunga, O. Fratu, G. Todoran, and V. Suciu. “Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things.” In 2013 19th International Conference on Control Systems and Computer Science (CSCS), 513–518, 2013. http://bit.ly/16wfNgv.

  • This paper proposes “a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities’ needs.”
  • The authors present a “framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed, analyzed and controlled by distributed cloud-based services.”

Townsend, Anthony. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. W. W. Norton & Company, 2013.

  • In this book, Townsend illustrates how “cities worldwide are deploying technology to address both the timeless challenges of government and the mounting problems posed by human settlements of previously unimaginable size and complexity.”
  • He also considers “the motivations, aspirations, and shortcomings” of the many stakeholders involved in the development of smart cities, and poses a new civics to guide these efforts.
  • He argues that smart cities are not made smart by various, soon-to-be-obsolete technologies built into its infrastructure, but how citizens use these ever-changing technologies to be “human-centered, inclusive and resilient.”

To stay current on recent writings and developments on Urban Analytics, please subscribe to the GovLab Digest.
Did we miss anything? Please submit reading recommendations to biblio@thegovlab.org or in the comments below.

Predicting crime, LAPD-style

Curated on Posted on by Stefaan Verhulst

The Guardian: “The Los Angeles Police Department, like many urban police forces today, is both heavily armed and thoroughly computerised. The Real-Time Analysis and Critical Response Division in downtown LA is its central processor. Rows of crime analysts and technologists sit before a wall covered in video screens stretching more than 10 metres wide. Multiple news broadcasts are playing simultaneously, and a real-time earthquake map is tracking the region’s seismic activity. Half-a-dozen security cameras are focused on the Hollywood sign, the city’s icon. In the centre of this video menagerie is an oversized satellite map showing some of the most recent arrests made across the city – a couple of burglaries, a few assaults, a shooting.

Advertisement

On a slightly smaller screen the division’s top official, Captain John Romero, mans the keyboard and zooms in on a comparably micro-scale section of LA. It represents just 500 feet by 500 feet. Over the past six months, this sub-block section of the city has seen three vehicle burglaries and two property burglaries – an atypical concentration. And, according to a new algorithm crunching crime numbers in LA and dozens of other cities worldwide, it’s a sign that yet more crime is likely to occur right here in this tiny pocket of the city.
The algorithm at play is performing what’s commonly referred to as predictive policing. Using years – and sometimes decades – worth of crime reports, the algorithm analyses the data to identify areas with high probabilities for certain types of crime, placing little red boxes on maps of the city that are streamed into patrol cars. “Burglars tend to be territorial, so once they find a neighbourhood where they get good stuff, they come back again and again,” Romero says. “And that assists the algorithm in placing the boxes.”
Romero likens the process to an amateur fisherman using a fish finder device to help identify where fish are in a lake. An experienced fisherman would probably know where to look simply by the fish species, time of day, and so on. “Similarly, a really good officer would be able to go out and find these boxes. This kind of makes the average guys’ ability to find the crime a little bit better.”
Predictive policing is just one tool in this new, tech-enhanced and data-fortified era of fighting and preventing crime. As the ability to collect, store and analyse data becomes cheaper and easier, law enforcement agencies all over the world are adopting techniques that harness the potential of technology to provide more and better information. But while these new tools have been welcomed by law enforcement agencies, they’re raising concerns about privacy, surveillance and how much power should be given over to computer algorithms.
P Jeffrey Brantingham is a professor of anthropology at UCLA who helped develop the predictive policing system that is now licensed to dozens of police departments under the brand name PredPol. “This is not Minority Report,” he’s quick to say, referring to the science-fiction story often associated with PredPol’s technique and proprietary algorithm. “Minority Report is about predicting who will commit a crime before they commit it. This is about predicting where and when crime is most likely to occur, not who will commit it.”…”

Privacy and Open Government

Curated on Posted on by Stefaan Verhulst

Paper by Teresa Scassa in Future Internet: “The public-oriented goals of the open government movement promise increased transparency and accountability of governments, enhanced citizen engagement and participation, improved service delivery, economic development and the stimulation of innovation. In part, these goals are to be achieved by making more and more government information public in reusable formats and under open licences. This paper identifies three broad privacy challenges raised by open government. The first is how to balance privacy with transparency and accountability in the context of “public” personal information. The second challenge flows from the disruption of traditional approaches to privacy based on a collapse of the distinctions between public and private sector actors. The third challenge is that of the potential for open government data—even if anonymized—to contribute to the big data environment in which citizens and their activities are increasingly monitored and profiled.”

Open for Business: How Open Data Can Help Achieve the G20 Growth Target

Curated on Posted on by Stefaan Verhulst

New Report commissioned by Omydiar Network on the Business Case for Open Data: “Economic analysis has confirmed the significant contribution to economic growth and productivity achievable through an open data agenda. Governments, the private sector, individuals and communities all stand to benefit from the innovation and information that will inform investment, drive the creation of new industries, and inform decision making and research. To mark a step change in the way valuable information is created and reused, the G20 should release information as open data.
In May 2014, Omidyar Network commissioned Lateral Economics to undertake economic analysis on the potential of open data to support the G20’s 2% growth target and illustrate how an open data agenda can make a significant contribution to economic growth and productivity. Combining all G20 economies, output could increase by USD 13 trillion cumulatively over the next five years. Implementation of open data policies would thus boost cumulative G20 GDP by around 1.1 percentage points (almost 55%) of the G20’s 2% growth target over five years.
Recommendations
Importantly, open data cuts across a number of this year’s G20 priorities: attracting private infrastructure investment, creating jobs and lifting participation, strengthening tax systems and fighting corruption. This memo suggests an open data thread that runs across all G20 priorities. The more data is opened, the more it can be used, reused, repurposed and built on—in combination with other data—for everyone’s benefit.
We call on G20 economies to sign up to the Open Data Charter.
The G20 should ensure that data released by G20 working groups and themes is in line with agreed open data standards. This will lead to more accountable, efficient, effective governments who are going further to expose inadequacy, fight corruption and spur innovation.
Data is a national resource and open data is a ‘win-win’ policy. It is about making more of existing resources. We know that the cost of opening data is smaller than the economic returns, which could be significant. Methods to respect privacy concerns must be taken into account. If this is done, as the public and private sector share of information grows, there will be increasing positive returns.
The G20 opportunity
This November, leaders of the G20 Member States will meet in Australia to drive forward commitments made in the St Petersburg G20 Leaders Declaration last September and to make firm progress on stimulating growth. Actions across the G20 will include increasing investment, lifting employment and participation, enhancing trade and promoting competition.
The resulting ‘Brisbane Action Plan’ will encapsulate all of these commitments with the aim of raising the level of G20 output by at least 2% above the currently projected level over the next five years. There are major opportunities for cooperative and collective action by G20 governments.
Governments should intensify the release of existing public sector data – both government and publicly funded research data. But much more can be done to promote open data than simply releasing more government data. In appropriate circumstances, governments can mandate public disclosure of private sector data (e.g. in corporate financial reporting).
Recommendations for action

  • G20 governments should adopt the principles of the Open Data Charter to encourage the building of stronger, more interconnected societies that better meet the needs of our citizens and allow innovation and prosperity to flourish.
  • G20 governments should adopt specific open data targets under each G20 theme, as illustrated below, such as releasing open data related to beneficial owners of companies, as well revenues from extractive industries
  • G20 governments should consider harmonizing licensing regimes across the G20
  • G20 governments should adopt metrics for measuring the quantity and quality of open data publication, e.g. using the Open Data Institute’s Open Data Certificates as a bottom-up mechanism for driving the adoption of common standards.

Illustrative G20 examples
Fiscal and monetary policy
Governments possess rich real time data that is not open or accessed by government macro-economic managers. G20 governments should:

  • Open up models that lie behind economic forecasts and help assess alternative policy settings;
  • Publish spending and contractual data to enable comparative shopping by government between government suppliers.

Anti corruption
Open data may directly contribute to reduced corruption by increasing the likelihood corruption will be detected. G20 governments should:

  • Release open data related to beneficial owners of companies as well as revenues from extractive industries,
  • Collaborate on harmonised technical standards that permit the tracing of international money flows – including the tracing of beneficial owners of commercial entities, and the comparison and reconciliation of transactions across borders.

Trade
Obtaining and using trade data from multiple jurisdictions is difficult. Access fees, specific licenses, and non-machine readable formats all involve large transaction costs. G20 governments should:

  • Harmonise open data policies related to trade data.
  • Use standard trade schema and formats.

Employment
Higher quality information on employment conditions would facilitate better matching of employees to organizations, producing greater job-satisfaction and improved productivity. G20 governments should:

  • Open up centralised job vacancy registers to provide new mechanisms for people to find jobs.
  • Provide open statistical information about the demand for skills in particular areas to help those supporting training and education to hone their offerings.

Energy
Open data will help reduce the cost of energy supply and improve energy efficiency. G20 governments should:

  • Provide incentives for energy companies to publish open data from consumers and suppliers to enable cost savings through optimizing energy plans.
  • Release energy performance certifications for buildings
  • Publish real-time energy consumption for government buildings.

Infrastructure
Current infrastructure asset information is fragmented and inefficient. Exposing current asset data would be a significant first step in understanding gaps and providing new insights. G20 governments should:

  • Publish open data on governments’ infrastructure assets and plans to better understand infrastructure gaps, enable greater efficiency and insights in infrastructure development and use and analyse cost/benefits.
  • Publish open infrastructure data, including contracts via Open Contracting Partnership, in a consistent and harmonised way across G20 countries…”

Big Data, My Data

Curated on Posted on by Stefaan Verhulst

Jane Sarasohn-Kahn  at iHealthBeat: “The routine operation of modern health care systems produces an abundance of electronically stored data on an ongoing basis,” Sebastian Schneeweis writes in a recent New England Journal of Medicine Perspective.
Is this abundance of data a treasure trove for improving patient care and growing knowledge about effective treatments? Is that data trove a Pandora’s black box that can be mined by obscure third parties to benefit for-profit companies without rewarding those whose data are said to be the new currency of the economy? That is, patients themselves?
In this emerging world of data analytics in health care, there’s Big Data and there’s My Data (“small data”). Who most benefits from the use of My Data may not actually be the consumer.
Big focus on Big Data. Several reports published in the first half of 2014 talk about the promise and perils of Big Data in health care. The Federal Trade Commission’s study, titled “Data Brokers: A Call for Transparency and Accountability,” analyzed the business practices of nine “data brokers,” companies that buy and sell consumers’ personal information from a broad array of sources. Data brokers sell consumers’ information to buyers looking to use those data for marketing, managing financial risk or identifying people. There are health implications in all of these activities, and the use of such data generally is not covered by HIPAA. The report discusses the example of a data segment called “Smoker in Household,” which a company selling a new air filter for the home could use to target-market to an individual who might seek such a product. On the downside, without the consumers’ knowledge, the information could be used by a financial services company to identify the consumer as a bad health insurance risk.
“Big Data and Privacy: A Technological Perspective,” a report from the President’s Office of Science and Technology Policy, considers the growth of Big Data’s role in helping inform new ways to treat diseases and presents two scenarios of the “near future” of health care. The first, on personalized medicine, recognizes that not all patients are alike or respond identically to treatments. Data collected from a large number of similar patients (such as digital images, genomic information and granular responses to clinical trials) can be mined to develop a treatment with an optimal outcome for the patients. In this case, patients may have provided their data based on the promise of anonymity but would like to be informed if a useful treatment has been found. In the second scenario, detecting symptoms via mobile devices, people wishing to detect early signs of Alzheimer’s Disease in themselves use a mobile device connecting to a personal couch in the Internet cloud that supports and records activities of daily living: say, gait when walking, notes on conversations and physical navigation instructions. For both of these scenarios, the authors ask, “Can the information about individuals’ health be sold, without additional consent, to third parties? What if this is a stated condition of use of the app? Should information go to the individual’s personal physicians with their initial consent but not a subsequent confirmation?”
The World Privacy Foundation’s report, titled “The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future,” describes the growing market for developing indices on consumer behavior, identifying over a dozen health-related scores. Health scores include the Affordable Care Act Individual Health Risk Score, the FICO Medication Adherence Score, various frailty scores, personal health scores (from WebMD and OneHealth, whose default sharing setting is based on the user’s sharing setting with the RunKeeper mobile health app), Medicaid Resource Utilization Group Scores, the SF-36 survey on physical and mental health and complexity scores (such as the Aristotle score for congenital heart surgery). WPF presents a history of consumer scoring beginning with the FICO score for personal creditworthiness and recommends regulatory scrutiny on the new consumer scores for fairness, transparency and accessibility to consumers.
At the same time these three reports went to press, scores of news stories emerged discussing the Big Opportunities Big Data present. The June issue of CFO Magazine published a piece called “Big Data: Where the Money Is.” InformationWeek published “Health Care Dives Into Big Data,” Motley Fool wrote about “Big Data’s Big Future in Health Care” and WIRED called “Cloud Computing, Big Data and Health Care” the “trifecta.”
Well-timed on June 5, the Office of the National Coordinator for Health IT’s Roadmap for Interoperability was detailed in a white paper, titled “Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure.” The document envisions the long view for the U.S. health IT ecosystem enabling people to share and access health information, ensuring quality and safety in care delivery, managing population health, and leveraging Big Data and analytics. Notably, “Building Block #3” in this vision is ensuring privacy and security protections for health information. ONC will “support developers creating health tools for consumers to encourage responsible privacy and security practices and greater transparency about how they use personal health information.” Looking forward, ONC notes the need for “scaling trust across communities.”
Consumer trust: going, going, gone? In the stakeholder community of U.S. consumers, there is declining trust between people and the companies and government agencies with whom people deal. Only 47% of U.S. adults trust companies with whom they regularly do business to keep their personal information secure, according to a June 6 Gallup poll. Furthermore, 37% of people say this trust has decreased in the past year. Who’s most trusted to keep information secure? Banks and credit card companies come in first place, trusted by 39% of people, and health insurance companies come in second, trusted by 26% of people.
Trust is a basic requirement for health engagement. Health researchers need patients to share personal data to drive insights, knowledge and treatments back to the people who need them. PatientsLikeMe, the online social network, launched the Data for Good project to inspire people to share personal health information imploring people to “Donate your data for You. For Others. For Good.” For 10 years, patients have been sharing personal health information on the PatientsLikeMe site, which has developed trusted relationships with more than 250,000 community members…”

How to Make Government Data Sites Better

Curated on Posted on by Stefaan Verhulst

Flowing Data: “Accessing government data from the source is frustrating. If you’ve done it, or at least tried to, you know the pain that is oddly formatted files, search that doesn’t work, and annotation that tells you nothing about the data in front of you.
The most frustrating part of the process is knowing how useful the data could be if only it were shared more simply. Unfortunately, ease-of-use is rarely the case, and we spend more time formatting and inspecting the data than we do actually putting it to use. Shouldn’t it be the other way around?
It’s this painstaking process that draws so much ire. It’s hard not to complain.
Maybe the people in charged of these sites just don’t know what’s going on. Or maybe they’re so overwhelmed by suck that they don’t know where to start. Or they’re unknowingly infected by the that-is-how-we’ve-always-done-it bug.
Whatever it may be, I need to think out loud about how to improve these sites. Empty complaints don’t help.
I use the Centers for Disease Control and Prevention as the test subject, but most of the things covered should easily generalize to other government sites (and non-government ones too). And I choose CDC not because they’re the worst but because they publish a lot of data that is of immediate and direct use to the general public.
I approach this from the point of view of someone who uses government data, beyond pulling a single data point from a spreadsheet. I’m also going to put on my Captain Obvious hat, because what seems obvious to some is apparently a black box to others.
Provide a useable data format
Sometimes it feels like government data is available in every format except the one that data users want. The worst one was when I downloaded a 2gb file, and upon unzipping it, I discovered it was a EXE file.
Data in PDF format is a kick in the face for people looking for CSV files. There might be ways to get the data out from PDFs, but it’s still a pain when you have more than a handful of files….
Useable data format is the most important, and if there’s just one thing you change, make it this.
(Raw data is fine too)
It’s rare to find raw government data, so it’s like striking gold when it actually happens. I realize you run into issues with data privacy, quality, missing data, etc. For these data sources, I appreciate the estimates with standard errors. However, the less aggregated (the more raw) you can provide, the better.
CSV for that too, please.
Never mind the fancy sharing tools
Not all government data is wedged into PDF files, and some of it is accessible via export tools that let you subset and layout your data exactly how you want it. The problem is that in an effort to please everyone, you end up with a tool shown on the left….
Tell people where to get the data
Get the things above done, and your government data site is exponentially better than it was before, but let’s keep going.
The navigation process to get to a dataset is incredibly convoluted, which makes it hard to find data and difficult to return to it….
Show visual previews
I’m all for visualization integrated with the data search tools. It always sucks when I spend time formatting data only to find that it wasn’t worth my time. Census Reporter is a fine example of how this might work.
That said, visual tools plus an upgrade to the previously mentioned things is a big undertaking, especially if you’re going to do it right. So I’m perfectly fine if you skip this step to focus your resources on data that’s easier to use and download. Leave the visualizing and analysis to us.
Decide what’s important, archive the rest
So much cruft. So many old documents. Broken links. Create an archive and highlight what people come to your site for.
Wrapping up
There’s plenty more stuff to update, especially once you start to work with the details, but this should be a good place to start. It’s a lot easier to point out what you can do to improve government data sharing than it is to actually do it of course. There are so many people, policies, and oh yes, politics, that it can be hard to change.”

How Long Is Too Long? The 4th Amendment and the Mosaic Theory

Curated on Posted on by Stefaan Verhulst

Law and Liberty Blog: “Volume 8.2 of the NYU Journal of Law and Liberty has been sent to the printer and physical copies will be available soon, but the articles in the issue are already available online here. One article that has gotten a lot of attention so far is by Steven Bellovin, Renee Hutchins, Tony Jebara, and Sebastian Zimmeck titled “When Enough is Enough: Location Tracking, Mosaic Theory, and Machine Learning.” A direct link to the article is here.
The mosaic theory is a modern corollary accepted by some academics – and the D.C. Circuit Court of Appeals in Maynard v. U.S. – as a twenty-first century extension of the Fourth Amendment’s prohibition on unreasonable searches of seizures. Proponents of the mosaic theory argue that at some point enough individual data collections, compiled and analyzed together, become a Fourth Amendment search. Thirty years ago the Supreme Court upheld the use of a tracking device for three days without a warrant, however the proliferation of GPS tracking in cars and smartphones has made it significantly easier for the police to access a treasure trove of information about our location at any given time.
It is easy to see why this theory has attracted some support. Humans are creatures of habit – if our public locations are tracked for a few days, weeks, or a month, it is pretty easy for machines to learn our ways and assemble a fairly detailed report for the government about our lives. Machines could basically predict when you will leave your house for work, what route you will take, when and where you go grocery shopping, all before you even do it, once it knows your habits. A policeman could observe you moving about in public without a warrant of course, but limited manpower will always reduce the probability of continuous mass surveillance. With current technology, a handful of trained experts could easily monitor hundreds of people at a time from behind a computer screen, and gather even more information than most searches requiring a warrant. The Supreme Court indicated a willingness to consider the mosaic theory in U.S. v. Jones, but has yet to embrace it…”

The article in Law & Liberty details the need to determine at which point machine learning creates an intrusion into our reasonable expectations of privacy, and even discusses an experiment that could be run to determine how long data collection can proceed before it is an intrusion. If there is a line at which individual data collection becomes a search, we need to discover where that line is. One of the articles’ authors, Steven Bollovin, has argued that the line is probably at one week – at that point your weekday and weekend habits would be known. The nation’s leading legal expert on criminal law, Professor Orin Kerr, fired back on the Volokh Conspiracy that Bollovin’s one week argument is not in line with previous iterations of the mosaic theory.

Humanitarians in the sky

Curated on Posted on by Stefaan Verhulst

Patrick Meier in the Guardian: “Unmanned aerial vehicles (UAVs) capture images faster, cheaper, and at a far higher resolution than satellite imagery. And as John DeRiggi speculates in “Drones for Development?” these attributes will likely lead to a host of applications in development work. In the humanitarian field that future is already upon us — so we need to take a rights-based approach to advance the discussion, improve coordination of UAV flights, and to promote regulation that will ensure safety while supporting innovation.
It was the unprecedentedly widespread use of civilian UAVs following typhoon Haiyan in the Philippines that opened my eyes to UAV use in post-disaster settings. I was in Manila to support the United Nations’ digital humanitarian efforts and came across new UAV projects every other day.
One team was flying rotary-wing UAVs to search for survivors among vast fields of debris that were otherwise inaccessible. Another flew fixed-wing UAVs around Tacloban to assess damage and produce high-quality digital maps. Months later, UAVs are still being used to support recovery and preparedness efforts. One group is working with local mayors to identify which communities are being overlooked in the reconstruction.
Humanitarian UAVs are hardly new. As far back as 2007, the World Food Program teamed up with the University of Torino to build humanitarian UAVs. But today UAVs are much cheaper, safer, and easier to fly. This means more people own personal UAVs. The distinguishing feature between these small UAVs and traditional remote control airplanes or helicopters is that they are intelligent. Most can be programmed to fly and land autonomously at designated locations. Newer UAVs also have on-board, flight-stabilization features that automatically adapt to changing winds, automated collision avoidance systems, and standard fail-safe mechanisms.
While I was surprised by the surge in UAV projects in the Philippines, I was troubled that none of these teams were aware of each other and that most were apparently not sharing their imagery with local communities. What happens when even more UAV teams show up following future disasters? Will they be accompanied by droves of drone journalists and “disaster tourists” equipped with personal UAVs? Will we see thousands of aerial disaster pictures and videos uploaded to social media rather than in the hands of local communities? What are the privacy implications? And what about empowering local communities to deploy their own UAVs?
There were many questions but few answers. So I launched the humanitarian UAV network (UAViators) to bridge the worlds of humanitarian professionals and UAV experts to address these questions. Our first priority was to draft a code of conduct for the use of UAVs in humanitarian settings to hold ourselves accountable while educating new UAV pilots before serious mistakes are made…”