Selected Readings on Personal Data: Security and Use

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of personal data was originally published in 2014.

Advances in technology have greatly increased the potential for policymakers to utilize the personal data of large populations for the public good. However, the proliferation of vast stores of useful data has also given rise to a variety of legislative, political, and ethical concerns surrounding the privacy and security of citizens’ personal information, both in terms of collection and usage. Challenges regarding the governance and regulation of personal data must be addressed in order to assuage individuals’ concerns regarding the privacy, security, and use of their personal information.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Cavoukian, Ann. “Personal Data Ecosystem (PDE) – A Privacy by Design Approach to an Individual’s Pursuit of Radical Control.” Privacy by Design, October 15, 2013. https://bit.ly/2S00Yfu.

  • In this paper, Cavoukian describes the Personal Data Ecosystem (PDE), an “emerging landscape of companies and organizations that believe individuals should be in control of their personal data, and make available a growing number of tools and technologies to enable this control.” She argues that, “The right to privacy is highly compatible with the notion of PDE because it enables the individual to have a much greater degree of control – “Radical Control” – over their personal information than is currently possible today.”
  • To ensure that the PDE reaches its privacy-protection potential, Cavouckian argues that it must practice The 7 Foundational Principles of Privacy by Design:
    • Proactive not Reactive; Preventative not Remedial
    • Privacy as the Default Setting
    • Privacy Embedded into Design
    • Full Functionality – Positive-Sum, not Zero-Sum
    • End-to-End Security – Full Lifecycle Protection
    • Visibility and Transparency – Keep it Open
    • Respect for User Privacy – Keep it User-Centric

Kirkham, T., S. Winfield, S. Ravet, and S. Kellomaki. “A Personal Data Store for an Internet of Subjects.” In 2011 International Conference on Information Society (i-Society). 92–97.  http://bit.ly/1alIGuT.

  • This paper examines various factors involved in the governance of personal data online, and argues for a shift from “current service-oriented applications where often the service provider is in control of the person’s data” to a person centric architecture where the user is at the center of personal data control.
  • The paper delves into an “Internet of Subjects” concept of Personal Data Stores, and focuses on implementation of such a concept on personal data that can be characterized as either “By Me” or “About Me.”
  • The paper also presents examples of how a Personal Data Store model could allow users to both protect and present their personal data to external applications, affording them greater control.

OECD. The 2013 OECD Privacy Guidelines. 2013. http://bit.ly/166TxHy.

  • This report is indicative of the “important role in promoting respect for privacy as a fundamental value and a condition for the free flow of personal data across borders” played by the OECD for decades. The guidelines – revised in 2013 for the first time since being drafted in 1980 – are seen as “[t]he cornerstone of OECD work on privacy.”
  • The OECD framework is built around eight basic principles for personal data privacy and security:
    • Collection Limitation
    • Data Quality
    • Purpose Specification
    • Use Limitation
    • Security Safeguards
    • Openness
    • Individual Participation
    • Accountability

Ohm, Paul. “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization.” UCLA Law Review 57, 1701 (2010). http://bit.ly/18Q5Mta.

  • This article explores the implications of the “astonishing ease” with which scientists have demonstrated the ability to “reidentify” or “deanonmize” supposedly anonymous personal information.
  • Rather than focusing exclusively on whether personal data is “anonymized,” Ohm offers five factors for governments and other data-handling bodies to use for assessing the risk of privacy harm: data-handling techniques, private versus public release, quantity, motive and trust.

Polonetsky, Jules and Omer Tene. “Privacy in the Age of Big Data: A Time for Big Decisions.” Stanford Law Review Online 64 (February 2, 2012): 63. http://bit.ly/1aeSbtG.

  • In this article, Tene and Polonetsky argue that, “The principles of privacy and data protection must be balanced against additional societal values such as public health, national security and law enforcement, environmental protection, and economic efficiency. A coherent framework would be based on a risk matrix, taking into account the value of different uses of data against the potential risks to individual autonomy and privacy.”
  • To achieve this balance, the authors believe that, “policymakers must address some of the most fundamental concepts of privacy law, including the definition of ‘personally identifiable information,’ the role of consent, and the principles of purpose limitation and data minimization.”

Shilton, Katie, Jeff Burke, Deborah Estrin, Ramesh Govindan, Mark Hansen, Jerry Kang, and Min Mun. “Designing the Personal Data Stream: Enabling Participatory Privacy in Mobile Personal Sensing”. TPRC, 2009. http://bit.ly/18gh8SN.

  • This article argues that the Codes of Fair Information Practice, which have served as a model for data privacy for decades, do not take into account a world of distributed data collection, nor the realities of data mining and easy, almost uncontrolled, dissemination.
  • The authors 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.”
  • In order to achieve this change in approach, the paper discusses three foundational design principles: primacy of participants, data legibility, and engagement of participants throughout the data life cycle.

Big Data, Privacy, and the Public Good

Curated on Posted on by Stefaan Verhulst

Forthcoming book and website by Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum (editors): “The overarching goal of the book is to identify ways in which vast new sets of data on human beings can be collected, integrated, and analysed to improve evidence based decision making while protecting confidentiality. …
Massive amounts of new data on human beings can now be accessed and analyzed.  Much has been made of the many uses of such data for pragmatic purposes, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet “big data” can also be harnessed to serve the public good: scientists can use new forms of data to do research that improves the lives of human beings, federal, state and local governments can use data to improve services and reduce taxpayer costs and public organizations can use information to advocate for public causes.
Much has also been made of the privacy and confidentiality issues associated with access. A survey of statisticians at the 2013 Joint Statistical Meeting found that the majority thought consumers should worry about privacy issues, and that an ethical framework should be in place to guide data scientists.  Yet there are many unanswered questions. What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?  What are the rules of engagement?  What are the best ways to provide access while protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss?
The goal of this book is to answer some of these questions.  The book’s authors paint an intellectual landscape that includes the legal, economic and statistical context necessary to frame the many privacy issues, including the value to the public of data access.   The authors also identify core practical approaches that use new technologies to simultaneously maximize the utility of data access while minimizing information risk. As is appropriate for such a new and evolving field, each chapter also identifies important questions that require future research.
The work in this book is also intended to be accessible to an audience broader than the academy. In addition to informing the public, we hope that the book will be useful to people trying to provide data access but protect confidentiality in the roles as data custodians for federal, state and local agencies, or decision makers on institutional review boards.”
 

It’s the Neoliberalism, Stupid: Why instrumentalist arguments for Open Access, Open Data, and Open Science are not enough.

Curated on Posted on by Stefaan Verhulst

Eric Kansa at LSE Blog: “…However, I’m increasingly convinced that advocating for openness in research (or government) isn’t nearly enough. There’s been too much of an instrumentalist justification for open data an open access. Many advocates talk about how it will cut costs and speed up research and innovation. They also argue that it will make research more “reproducible” and transparent so interpretations can be better vetted by the wider community. Advocates for openness, particularly in open government, also talk about the wonderful commercial opportunities that will come from freeing research…
These are all very big policy issues, but they need to be asked if the Open Movement really stands for reform and not just a further expansion and entrenchment of Neoliberalism. I’m using the term “Neoliberalism” because it resonates as a convenient label for describing how and why so many things seem to suck in Academia. Exploding student debt, vanishing job security, increasing compensation for top administrators, expanding bureaucracy and committee work, corporate management methodologies (Taylorism), and intensified competition for ever-shrinking public funding all fall under the general rubric of Neoliberalism. Neoliberal universities primarily serve the needs of commerce. They need to churn out technically skilled human resources (made desperate for any work by high loads of debt) and easily monetized technical advancements….
“Big Data,” “Data Science,” and “Open Data” are now hot topics at universities. Investments are flowing into dedicated centers and programs to establish institutional leadership in all things related to data. I welcome the new Data Science effort at UC Berkeley to explore how to make research data professionalism fit into the academic reward systems. That sounds great! But will these new data professionals have any real autonomy in shaping how they conduct their research and build their careers? Or will they simply be part of an expanding class of harried and contingent employees- hired and fired through the whims of creative destruction fueled by the latest corporate-academic hype-cycle?
Researchers, including #AltAcs and “data professionals”, need  a large measure of freedom. Miriam Posner’s discussion about the career and autonomy limits of Alt-academic-hood help highlight these issues. Unfortunately, there’s only one area where innovation and failure seem survivable, and that’s the world of the start-up. I’ve noticed how the “Entrepreneurial Spirit” gets celebrated lots in this space. I’m guilty of basking in it myself (10 years as a quasi-independent #altAc in a nonprofit I co-founded!).
But in the current Neoliberal setting, being an entrepreneur requires a singular focus on monetizing innovation. PeerJ and Figshare are nice, since they have business models that less “evil” than Elsevier’s. But we need to stop fooling ourselves that the only institutions and programs that we can and should sustain are the ones that can turn a profit. For every PeerJ or Figshare (and these are ultimately just as dependent on continued public financing of research as any grant-driven project), we also need more innovative organizations like the Internet Archive, wholly dedicated to the public good and not the relentless pressure to commoditize everything (especially their patrons’ privacy). We need to be much more critical about the kinds of programs, organizations, and financing strategies we (as a society) can support. I raised the political economy of sustainability issue at a recent ThatCamp and hope to see more discussion.
In reality so much of the Academy’s dysfunctions are driven by our new Gilded Age’s artificial scarcity of money. With wealth concentrated in so few hands, it is very hard to finance risk taking and entreprenurialism in the scholarly community, especially to finance any form of entrepreneurialism that does not turn a profit in a year or two.
Open Access and Open Data will make so much more of a difference if we had the same kind of dynamism in the academic and nonprofit sector as we have in the for-profit start-up sector. After all, Open Access and Open Data can be key enablers to allow much broader participation in research and education. However, broader participation still needs to be financed: you cannot eat an open access publication. We cannot gloss over this key issue.
We need more diverse institutional forms so that researchers can find (or found) the kinds of organizations that best channel their passions into contributions that enrich us all. We need more diverse sources of financing (new foundations, better financed Kickstarters) to connect innovative ideas with the capital needed to see them implemented. Such institutional reforms will make life in the research community much more livable, creative, and dynamic. It would give researchers more options for diverse and varied career trajectories (for-profit or not-for-profit) suited to their interests and contributions.
Making the case to reinvest in the public good will require a long, hard slog. It will be much harder than the campaign for Open Access and Open Data because it will mean contesting Neoliberal ideologies and constituencies that are deeply entrenched in our institutions. However, the constituencies harmed by Neoliberalism, particularly the student community now burdened by over $1 trillion in debt and the middle class more generally, are much larger and very much aware that something is badly amiss. As we celebrate the impressive strides made by the Open Movement in the past year, it’s time we broaden our goals to tackle the needs for wider reform in the financing and organization of research and education.
This post originally appeared on Digging Digitally and is reposted under a CC-BY license.”

Big Data’s Dangerous New Era of Discrimination

Curated on Posted on by Stefaan Verhulst

Michael Schrage in HBR blog: “Congratulations. You bought into Big Data and it’s paying off Big Time. You slice, dice, parse and process every screen-stroke, clickstream, Like, tweet and touch point that matters to your enterprise. You now know exactly who your best — and worst — customers, clients, employees and partners are.  Knowledge is power.  But what kind of power does all that knowledge buy?
Big Data creates Big Dilemmas. Greater knowledge of customers creates new potential and power to discriminate. Big Data — and its associated analytics — dramatically increase both the dimensionality and degrees of freedom for detailed discrimination. So where, in your corporate culture and strategy, does value-added personalization and segmentation end and harmful discrimination begin?
Let’s say, for example, that your segmentation data tells you the following:
Your most profitable customers by far are single women between the ages of 34 and 55 closely followed by “happily married” women with at least one child. Divorced women are slightly more profitable than “never marrieds.” Gay males — single and in relationships — are also disproportionately profitable. The “sweet spot” is urban and 28 to 50. These segments collectively account for roughly two-thirds of your profitability.  (Unexpected factoid: Your most profitable customers are overwhelmingly Amazon Prime subscriber. What might that mean?)
Going more granular, as Big Data does, offers even sharper ethno-geographic insight into customer behavior and influence:

  • Single Asian, Hispanic, and African-American women with urban post codes are most likely to complain about product and service quality to the company. Asian and Hispanic complainers happy with resolution/refund tend to be in the top quintile of profitability. African-American women do not.
  • Suburban Caucasian mothers are most likely to use social media to share their complaints, followed closely by Asian and Hispanic mothers. But if resolved early, they’ll promote the firm’s responsiveness online.
  • Gay urban males receiving special discounts and promotions are the most effective at driving traffic to your sites.

My point here is that these data are explicit, compelling and undeniable. But how should sophisticated marketers and merchandisers use them?
Campaigns, promotions and loyalty programs targeting women and gay males seem obvious. But should Asian, Hispanic and white females enjoy preferential treatment over African-American women when resolving complaints? After all, they tend to be both more profitable and measurably more willing to effectively use social media. Does it make more marketing sense encouraging African-American female customers to become more social media savvy? Or are resources better invested in getting more from one’s best customers? Similarly, how much effort and ingenuity flow should go into making more gay male customers better social media evangelists? What kinds of offers and promotions could go viral on their networks?…
Of course, the difference between price discrimination and discrimination positively correlated with gender, ethnicity, geography, class, personality and/or technological fluency is vanishingly small. Indeed, the entire epistemological underpinning of Big Data for business is that it cost-effectively makes informed segmentation and personalization possible…..
But the main source of concern won’t be privacy, per se — it will be whether and how companies and organizations like your own use Big Data analytics to justify their segmentation/personalization/discrimination strategies. The more effective Big Data analytics are in profitably segmenting and serving customers, the more likely those algorithms will be audited by regulators or litigators.
Tomorrow’s Big Data challenge isn’t technical; it’s whether managements have algorithms and analytics that are both fairly transparent and transparently fair. Big Data champions and practitioners had better be discriminating about how discriminating they want to be.”

The Age of ‘Infopolitics’

Curated on Posted on by Stefaan Verhulst

Colin Koopman in the New York Times: “We are in the midst of a flood of alarming revelations about information sweeps conducted by government agencies and private corporations concerning the activities and habits of ordinary Americans. After the initial alarm that accompanies every leak and news report, many of us retreat to the status quo, quieting ourselves with the thought that these new surveillance strategies are not all that sinister, especially if, as we like to say, we have nothing to hide.
One reason for our complacency is that we lack the intellectual framework to grasp the new kinds of political injustices characteristic of today’s information society. Everyone understands what is wrong with a government’s depriving its citizens of freedom of assembly or liberty of conscience. Everyone (or most everyone) understands the injustice of government-sanctioned racial profiling or policies that produce economic inequality along color lines. But though nearly all of us have a vague sense that something is wrong with the new regimes of data surveillance, it is difficult for us to specify exactly what is happening and why it raises serious concern, let alone what we might do about it.
Our confusion is a sign that we need a new way of thinking about our informational milieu. What we need is a concept of infopolitics that would help us understand the increasingly dense ties between politics and information. Infopolitics encompasses not only traditional state surveillance and data surveillance, but also “data analytics” (the techniques that enable marketers at companies like Target to detect, for instance, if you are pregnant), digital rights movements (promoted by organizations like the Electronic Frontier Foundation), online-only crypto-currencies (like Bitcoin or Litecoin), algorithmic finance (like automated micro-trading) and digital property disputes (from peer-to-peer file sharing to property claims in the virtual world of Second Life). These are only the tip of an enormous iceberg that is drifting we know not where.
Surveying this iceberg is crucial because atop it sits a new kind of person: the informational person. Politically and culturally, we are increasingly defined through an array of information architectures: highly designed environments of data, like our social media profiles, into which we often have to squeeze ourselves. The same is true of identity documents like your passport and individualizing dossiers like your college transcripts. Such architectures capture, code, sort, fasten and analyze a dizzying number of details about us. Our minds are represented by psychological evaluations, education records, credit scores. Our bodies are characterized via medical dossiers, fitness and nutrition tracking regimens, airport security apparatuses. We have become what the privacy theorist Daniel Solove calls “digital persons.” As such we are subject to infopolitics (or what the philosopher Grégoire Chamayou calls “datapower,” the political theorist Davide Panagia “datapolitik” and the pioneering thinker Donna Haraway “informatics of domination”).
Today’s informational person is the culmination of developments stretching back to the late 19th century. It was in those decades that a number of early technologies of informational identity were first assembled. Fingerprinting was implemented in colonial India, then imported to Britain, then exported worldwide. Anthropometry — the measurement of persons to produce identifying records — was developed in France in order to identify recidivists. The registration of births, which has since become profoundly important for initiating identification claims, became standardized in many countries, with Massachusetts pioneering the way in the United States before a census initiative in 1900 led to national standardization. In the same era, bureaucrats visiting rural districts complained that they could not identify individuals whose names changed from context to context, which led to initiatives to universalize standard names. Once fingerprints, biometrics, birth certificates and standardized names were operational, it became possible to implement an international passport system, a social security number and all other manner of paperwork that tells us who someone is. When all that paper ultimately went digital, the reams of data about us became radically more assessable and subject to manipulation, which has made us even more informational.
We like to think of ourselves as somehow apart from all this information. We are real — the information is merely about us. But what is it that is real? What would be left of you if someone took away all your numbers, cards, accounts, dossiers and other informational prostheses? Information is not just about you — it also constitutes who you are….”

Big Data and the Future of Privacy

Curated on Posted on by Stefaan Verhulst

John Podesta at the White House blog: “Last Friday, the President spoke to the American people, and the international community, about how to keep us safe from terrorism in a changing world while upholding America’s commitment to liberty and privacy that our values and Constitution require. Our national security challenges are real, but that is surely not the only space where changes in technology are altering the landscape and challenging conceptions of privacy.
That’s why in his speech, the President asked me to lead a comprehensive review of the way that “big data” will affect the way we live and work; the relationship between government and citizens; and how public and private sectors can spur innovation and maximize the opportunities and free flow of this information while minimizing the risks to privacy. I will be joined in this effort by Secretary of Commerce Penny Pritzker, Secretary of Energy Ernie Moniz, the President’s Science Advisor John Holdren, the President’s Economic Advisor Gene Sperling and other senior government officials.
I would like to explain a little bit more about the review, its scope, and what you can expect over the next 90 days.
We are undergoing a revolution in the way that information about our purchases, our conversations, our social networks, our movements, and even our physical identities are collected, stored, analyzed and used. The immense volume, diversity and potential value of data will have profound implications for privacy, the economy, and public policy. The working group will consider all those issues, and specifically how the present and future state of these technologies might motivate changes in our policies across a range of sectors.
When we complete our work, we expect to deliver to the President a report that anticipates future technological trends and frames the key questions that the collection, availability, and use of “big data” raise – both for our government, and the nation as a whole. It will help identify technological changes to watch, whether those technological changes are addressed by the U.S.’s current policy framework and highlight where further government action, funding, research and consideration may be required.
This is going to be a collaborative effort. The President’s Council of Advisors on Science and Technology (PCAST) will conduct a study to explore in-depth the technological dimensions of the intersection of big data and privacy, which will feed into this broader effort. Our working group will consult with industry, civil liberties groups, technologists, privacy experts, international partners, and other national and local government officials on the significance of and future for these technologies. Finally, we will be working with a number of think tanks, academic institutions, and other organizations around the country as they convene stakeholders to discuss these very issues and questions. Likewise, many abroad are analyzing and responding to the challenge and seizing the opportunity of big data. These discussions will help to inform our study.
While we don’t expect to answer all these questions, or produce a comprehensive new policy in 90 days, we expect this work to serve as the foundation for a robust and forward-looking plan of action. Check back on this blog for updates on how you can get involved in the debate and for status updates on our progress.”

Open Development (Networked Innovations in International Development)

Curated on Posted on by Stefaan Verhulst

New book edited by Matthew L. Smith and Katherine M. A. Reilly (Foreword by Yochai Benkler) : “The emergence of open networked models made possible by digital technology has the potential to transform international development. Open network structures allow people to come together to share information, organize, and collaborate. Open development harnesses this power, to create new organizational forms and improve people’s lives; it is not only an agenda for research and practice but also a statement about how to approach international development. In this volume, experts explore a variety of applications of openness, addressing challenges as well as opportunities.
Open development requires new theoretical tools that focus on real world problems, consider a variety of solutions, and recognize the complexity of local contexts. After exploring the new theoretical terrain, the book describes a range of cases in which open models address such specific development issues as biotechnology research, improving education, and access to scholarly publications. Contributors then examine tensions between open models and existing structures, including struggles over privacy, intellectual property, and implementation. Finally, contributors offer broader conceptual perspectives, considering processes of social construction, knowledge management, and the role of individual intent in the development and outcomes of social models.”

Entrepreneurs Shape Free Data Into Money

Curated on Posted on by Stefaan Verhulst
Angus Loten in the Wall Street Journal: “More cities are putting information on everything from street-cleaning schedules to police-response times and restaurant inspection reports in the public domain, in the hope that people will find a way to make money off the data.
Supporters of such programs often see them as a local economic stimulus plan, allowing software developers and entrepreneurs in cities ranging from San Francisco to South Bend, Ind., to New York, to build new businesses based on the information they get from government websites.
When Los Angeles Mayor Eric Garcetti issued an executive directive last month to launch the city’s open-data program, he cited entrepreneurs and businesses as important beneficiaries. Open-data promotes innovation and “gives companies, individuals, and nonprofit organizations the opportunity to leverage one of government’s greatest assets: public information,” according to the Dec. 18 directive.
A poster child for the movement might be 34-year-old Matt Ehrlichman of Seattle, who last year built an online business in part using Seattle work permits, professional licenses and other home-construction information gathered up by the city’s Department of Planning and Development.
While his website is free, his business, called Porch.com, has more than 80 employees and charges a $35 monthly fee to industry professionals who want to boost the visibility of their projects on the site.
The site gathers raw public data—such as addresses for homes under renovation, what they are doing, who is doing the work and how much they are charging—and combines it with photos and other information from industry professionals and homeowners. It then creates a searchable database for users to compare ideas and costs for projects near their own neighborhood.
…Ian Kalin, director of open-data services at Socrata, a Seattle-based software firm that makes the back-end applications for many of these government open-data sites, says he’s worked with hundreds of companies that were formed around open data.
Among them is Climate Corp., a San Francisco-based firm that collects weather and yield-forecasting data to help farmers decide when and where to plant crops. Launched in 2006, the firm was acquired in October by Monsanto Co. MON -2.90% , the seed-company giant, for $930 million.
Overall, the rate of new business formation declined nationally between 2006 and 2010. But according to the latest data from the Ewing Marion Kauffman Foundation, an entrepreneurship advocacy group in Kansas City, Mo., the rate of new business formation in Seattle in 2011 rose 9.41% in 2011, compared with the national average of 3.9%.
Other cities where new business formation was ahead of the national average include Chicago, Austin, Texas, Baltimore, and South Bend, Ind.—all cities that also have open-data programs. Still, how effective the ventures are in creating jobs is difficult to gauge.
One wrinkle: privacy concerns about the potential for information—such as property tax and foreclosure data—to be misused.
Some privacy advocates fear that government data that include names, addresses and other sensitive information could be used by fraudsters to target victims.”

The Emergence Of The Connected City

Curated on Posted on by Stefaan Verhulst

Glen Martin at Forbes: “If the modern city is a symbol for randomness — even chaos — the city of the near future is shaping up along opposite metaphorical lines. The urban environment is evolving rapidly, and a model is emerging that is more efficient, more functional, more — connected, in a word.
This will affect how we work, commute, and spend our leisure time. It may well influence how we relate to one another, and how we think about the world. Certainly, our lives will be augmented: better public transportation systems, quicker responses from police and fire services, more efficient energy consumption. But there could also be dystopian impacts: dwindling privacy and imperiled personal data. We could even lose some of the ferment that makes large cities such compelling places to live; chaos is stressful, but it can also be stimulating.
It will come as no surprise that converging digital technologies are driving cities toward connectedness. When conjoined, ISM band transmitters, sensors, and smart phone apps form networks that can make cities pretty darn smart — and maybe more hygienic. This latter possibility, at least, is proposed by Samrat Saha of the DCI Marketing Group in Milwaukee. Saha suggests “crowdsourcing” municipal trash pick-up via BLE modules, proximity sensors and custom mobile device apps.
“My idea is a bit tongue in cheek, but I think it shows how we can gain real efficiencies in urban settings by gathering information and relaying it via the Cloud,” Saha says. “First, you deploy sensors in garbage cans. Each can provides a rough estimate of its fill level and communicates that to a BLE 112 Module.”
As pedestrians who have downloaded custom “garbage can” apps on their BLE-capable iPhone or Android devices pass by, continues Saha, the information is collected from the module and relayed to a Cloud-hosted service for action — garbage pick-up for brimming cans, in other words. The process will also allow planners to optimize trash can placement, redeploying receptacles from areas where need is minimal to more garbage-rich environs….
Garbage can connectivity has larger implications than just, well, garbage. Brett Goldstein, the former Chief Data and Information Officer for the City of Chicago and a current lecturer at the University of Chicago, says city officials found clear patterns between damaged or missing garbage cans and rat problems.
“We found areas that showed an abnormal increase in missing or broken receptacles started getting rat outbreaks around seven days later,” Goldstein said. “That’s very valuable information. If you have sensors on enough garbage cans, you could get a temporal leading edge, allowing a response before there’s a problem. In urban planning, you want to emphasize prevention, not reaction.”
Such Cloud-based app-centric systems aren’t suited only for trash receptacles, of course. Companies such as Johnson Controls are now marketing apps for smart buildings — the base component for smart cities. (Johnson’s Metasys management system, for example, feeds data to its app-based Paoptix Platform to maximize energy efficiency in buildings.) In short, instrumented cities already are emerging. Smart nodes — including augmented buildings, utilities and public service systems — are establishing connections with one another, like axon-linked neurons.
But Goldstein, who was best known in Chicago for putting tremendous quantities of the city’s data online for public access, emphasizes instrumented cities are still in their infancy, and that their successful development will depend on how well we “parent” them.
“I hesitate to refer to ‘Big Data,’ because I think it’s a terribly overused term,” Goldstein said. “But the fact remains that we can now capture huge amounts of urban data. So, to me, the biggest challenge is transitioning the fields — merging public policy with computer science into functional networks.”…”

6 New Year’s Strategies for Open Data Entrepreneurs

Curated on Posted on by Stefaan Verhulst

The GovLab’s Senior Advisor Joel Gurin: “Open Data has fueled a wide range of startups, including consumer-focused websites, business-to-business services, data-management tech firms, and more. Many of the companies in the Open Data 500 study are new ones like these. New Year’s is a classic time to start new ventures, and with 2014 looking like a hot year for Open Data, we can expect more startups using this abundant, free resource. For my new book, Open Data Now, I interviewed dozens of entrepreneurs and distilled six of the basic strategies that they’ve used.
1. Learn how to add value to free Open Data. We’re seeing an inversion of the value proposition for data. It used to be that whoever owned the data—particularly Big Data—had greater opportunities than those who didn’t. While this is still true in many areas, it’s also clear that successful businesses can be built on free Open Data that anyone can use. The value isn’t in the data itself but rather in the analytical tools, expertise, and interpretation that’s brought to bear. One oft-cited example: The Climate Corporation, which built a billion-dollar business out of government weather and satellite data that’s freely available for use.
2. Focus on big opportunities: health, finance, energy, education. A business can be built on just about any kind of Open Data. But the greatest number of startup opportunities will likely be in the four big areas where the federal government is focused on Open Data release. Last June’s Health Datapalooza showcased the opportunities in health. Companies like Opower in energy, GreatSchools in education, and Calcbench, SigFig, and Capital Cube in finance are examples in these other major sectors.
3. Explore choice engines and Smart Disclosure apps. Smart Disclosure – releasing data that consumers can use to make marketplace choices – is a powerful tool that can be the basis for a new sector of online startups. No one, it seems, has quite figured out how to make this form of Open Data work best, although sites like CompareTheMarket in the UK may be possible models. Business opportunities await anyone who can find ways to provide these much-needed consumer services. One example: Kayak, which competed in the crowded travel field by providing a great consumer interface, and which was sold to Priceline for $1.8 billion last year.
4. Help consumers tap the value of personal data. In a privacy-conscious society, more people will be interested in controlling their personal data and sharing it selectively for their own benefit. The value of personal data is just being recognized, and opportunities remain to be developed. There are business opportunities in setting up and providing “personal data vaults” and more opportunity in applying the many ways they can be used. Personal and Reputation.com are two leaders in this field.
5. Provide new data solutions to governments at all levels. Government datasets at the federal, state, and local level can be notoriously difficult to use. The good news is that these governments are now realizing that they need help. Data management for government is a growing industry, as Socrata, OpenGov, 3RoundStones, and others are finding, while companies like Enigma.io are turning government data into a more usable resource.
6. Look for unusual Open Data opportunities. Building a successful business by gathering data on restaurant menus and recipes is not an obvious route to success. But it’s working for Food Genius, whose founders showed a kind of genius in tapping an opportunity others had missed. While the big areas for Open Data are becoming clear, there are countless opportunities to build more niche businesses that can still be highly successful. If you have expertise in an area and see a customer need, there’s an increasingly good chance that the Open Data to help meet that need is somewhere to be found.”