Collection

The Architecture of Privacy

Curated on January 28, 2015November 15, 2018 by Stefaan Verhulst

Book by Courtney Bowman, Ari Gesher, John K Grant, and Daniel Slate: “Technology’s influence on privacy has become a matter of everyday concern for millions of people, from software architects designing new products to political leaders and consumer groups. This book explores the issue from the perspective of technology itself: how privacy-protective features can become a core part of product functionality, rather than added on late in the development process.
The Architecture of Privacy will not only help empower software engineers, but also show policymakers, academics, and advocates that, through an arsenal of technical tools, engineers can form the building blocks of nuanced policies that maximize privacy protection and utility—a menu of what to demand in new technology.
Topics include:

How technology and privacy policy interact and influence one another
Privacy concerns about government and corporate data collection practices
Approaches to federated systems as a component of privacy-protecting architecture
Alternative approaches to compartmentalized access to data
Methods to limit the amount of data revealed in searches, sidestepping all-or-nothing choices
Techniques for data purging and responsible data retention
Keeping and analyzing audit logs as part of a program of comprehensive system oversight
… (More)“

The Cathedral of Computation

Curated on January 28, 2015November 15, 2018 by Stefaan Verhulst

Ian Bogost at the Atlantic: “We’re not living in an algorithmic culture so much as a computational theocracy. Algorithms are everywhere, supposedly. We are living in an “algorithmic culture,” to use the author and communication scholar Ted Striphas’s name for it. Google’s search algorithms determine how we access information. Facebook’s News Feed algorithms determine how we socialize. Netflix’s and Amazon’s collaborative filtering algorithms choose products and media for us. You hear it everywhere. “Google announced a change to its algorithm,” a journalist reports. “We live in a world run by algorithms,” a TED talk exhorts. “Algorithms rule the world,” a news report threatens. Another upgrades rule to dominion: “The 10 Algorithms that Dominate Our World.”…
It’s part of a larger trend. The scientific revolution was meant to challenge tradition and faith, particularly a faith in religious superstition. But today, Enlightenment ideas like reason and science are beginning to flip into their opposites. Science and technology have become so pervasive and distorted, they have turned into a new type of theology.
The worship of the algorithm is hardly the only example of the theological reversal of the Enlightenment—for another sign, just look at the surfeit of nonfiction books promising insights into “The Science of…” anything, from laughter to marijuana. But algorithms hold a special station in the new technological temple because computers have become our favorite idols….
Once you adopt skepticism toward the algorithmic- and the data-divine, you can no longer construe any computational system as merely algorithmic. Think about Google Maps, for example. It’s not just mapping software running via computer—it also involves geographical information systems, geolocation satellites and transponders, human-driven automobiles, roof-mounted panoramic optical recording systems, international recording and privacy law, physical- and data-network routing systems, and web/mobile presentational apparatuses. That’s not algorithmic culture—it’s just, well, culture….(More).”

Would You Share Private Data for the Good of City Planning?

Curated on January 28, 2015August 3, 2018 by Stefaan Verhulst

Henry Grabar at NextCity: “The proliferation of granular data on automobile movement, drawn from smartphones, cab companies, sensors and cameras, is sharpening our sense of how cars travel through cities. Panglossian seers believe the end of traffic jams is nigh.
This information will change cities beyond their roads. Real-time traffic data may lead to reworked intersections and new turning lanes, but understanding cars is in some ways a stand-in for understanding people. There’s traffic as traffic and traffic as proxy, notes Brett Goldstein, an urban science fellow at the University of Chicago who served as that city’s first data officer from 2011 to 2013. “We’d be really naive, in thinking about how we make cities better,” he says, “to only consider traffic for what it is.”
Even a small subset of a city’s car data goes a long way. Consider the raft of discrete findings that have emerged from the records of New York City taxis.
Researchers at the Massachusetts Institute of Technology, led by Paolo Santi, showed that cab-sharing could reduce taxi mileage by 40 percent. Their counterparts at NYU, led by Claudio Silva, mapped activity around hubs like train stations and airports and during hurricanes.
“You start to build actual models of how people move, and where they move,” observes Silva, the head of disciplines at NYU’s Center for Science and Urban Progress (CUSP). “The uses of this data for non-traffic engineering are really substantial.”…
Many of these ideas are hypothetical, for the moment, because so-called “granular” data is so hard to come by. That’s one reason the release of New York’s taxi cab data spurred so many studies — it’s an oasis of information in a desert of undisclosed records. Corporate entreaties, like Uber’s pending data offering to Boston, don’t always meet researchers’ standards. “It’s going to be a lot of superficial data, and it’s not clear how usable it’ll be at this point,” explains Sarah Kaufman, the digital manager at NYU’s Rudin Center for Transportation….
Yet Americans seem much more alarmed by the collection of location data than other privacy breaches.
How can data utopians convince the hoi polloi to share their comings and goings? One thought: Make them secure. Mike Flowers, the founder of New York City’s Office of Data Analytics and a fellow at NYU’s CUSP, told me it might be time to consider establishing a quasi-governmental body that people would trust to make their personal data anonymous before they are channeled into government projects. (New York City’s Taxi and Limousine Commission did not do a very good job at this, which led to Gawker publishing a dozen celebrity cab rides.)
Another idea is to frame open data as a beneficial trade-off. “When people provide information, they want to realize the benefit of the information,” Goldstein says.
Users tell the routing company Waze where they are and get a smoother commute in return. Progressive Insurance offers drivers a “Snapshot” tracker. If it likes the way you drive, the company will lower your rates. It’s not hard to imagine that, in the long run, drivers will be penalized for refusing such a device…. (More).”

Opening travel spending through civic intelligence, participation and co-creation

Curated on January 28, 2015May 29, 2019 by Stefaan Verhulst

Joel Salas Suárez at the Open Government Partnership Blog: “When we were appointed by the Senate as Commissioners of the Access to Information Institute in Mexico (IFAI), we identified two high profile issues that had negatively affected the Institute’s image: the acquisition of its new building and the lack of transparency on international travel expenditure of the former Commissioners.

IFAI has to lead by example, so my fellow commissioners and I decided to tackle these two problems with transparency actions to send a clear message to the Mexican society and the international community in our first hundred days in office. First we created the website sede.ifai.mx to publish all the information about the new building procurement (a 45.6 million USD lease). Secondly, we decided to start our first civic innovation project, a joint venture with civil society organizations, to find the best way to publish information related to travel spending by IFAI’s public servants.
Travel expenditure of IFAI is comparatively smaller. During 2013 it allotted to 186,760 USD, 0.5% of the Institute’s budget (38.2 million USD). However, this expenditure has historically been of public interest and it should be. According to the 2013 Mexican Government Expenditure Review (the latest available) the Federal Level (Executive, Legislative and Judicial Powers, and Autonomous organs) spent close to 633 million USD in official travel (Chapter 3000, concept 3700). Therefore, we decided to tackle the problem and design a platform that would allow us to effectively publish information related to the public money spent on travel by public officials and the results obtained during these trips.
In order to do this, we worked with civil society experts in public participation, accountability and technology, Codeando México, SocialTIC and IMCO. Together we launched a public challenge to create an open source web application to publish information on official travel spending.
The challenge #RetoViajesTransparentes was a very successful experience. Close to a hundred participants registered 14 projects that competed to develop an app that IFAI would officially use and to win a 3,500 USD prize. The jury selected 3 finalists, who presented their projects on a public Google Hangout. The winner app is named Viajes Claros and is being used to publish travel expenditure information of IFAI at viajesclaros.ifai.mx.
This challenge has allowed us to shift focus from the inputs of official travel (i.e. the money spent) to the outputs or results attained in each trip. Viajes Claros opens relevant information to understand and evaluate the activities performed by the public servants during their trips. It also allowed us to co-create with society an open source tool that can be replicated in Mexico and other countries….(More)”.

The Modern Beauty of 19th-Century Data Visualizations

Curated on January 28, 2015October 30, 2018 by Stefaan Verhulst

Laura Bliss at CityLab: “The Library of Congress‘ online presence is a temple of American history, an unmatched, searchable collection of digitized photographs, maps, recordings, sheet music, and documents in the millions, dating back to the 15th century.

Sifting through these treasures isn’t so easy, though. When you do manage the clunky search interface and stumble across a gorgeous 1870s statistical atlas, it’s hard to zoom in closely on its pages and properly marvel at the antique gem.
Problem solved, thanks to the info-nerds at Vintage Visualizations, a project of the Brooklyn Brainery. They’ve reproduced a number of the LOC’s Civil War-era data visualizations in high-quality poster prints, and they are mouthwateringly cool. For example, I really wish we still ranked city populations like this chart does, which traces a century of census data in colorful Jenga towers (NYC, forever the biggest apple!):

Behold, the ratio of “church accommodation” by state, circa 1870, displayed like wallpaper swatches….(More):

Citizen Science in America’s DNA

Curated on January 27, 2015August 15, 2018 by Stefaan Verhulst

Keynote by NOAA Chief Scientist, Dr. Richard Spinrad at the forum entitled, Tracking a Changing Climate: “Citizen science is part of America’s DNA. For centuries, citizens not trained in science have helped shaped our understanding of Earth.
Thomas Jefferson turned Lewis and Clark into citizen scientists when he asked them to explore the landscape, wildlife and weather during their journeys of the West.They investigated plants, animals and geography, and came back with maps, sketches and journals. These new data were some of the first pieces of environmental intelligence defining our young nation. President Jefferson instilled citizen science in my own agency’s DNA by creating the Survey of the Coast, a NOAA legacy agency focused on charting and protecting the entire coast of our Nation.
The National Weather Service’s Cooperative Observer Program, begun in 1890, is an outstanding example of citizen science. Last year, NOAA honored an observer who has provided weather observations every day for 80 years. Volunteer citizen scientists have transcribed more than 68,000 pages of Arctic ship logs, adding to the long-term climate record by populating a database with historic weather and sea ice observations. Also, citizen scientists are providing new estimates of cyclone intensity by interpreting satellite images.
There is tremendous value in the capability of citizen scientists to feed local data into their own communities’ forecasts. In September 2013, for example, formal observation systems and tracking instruments were washed out when extreme floods struck Colorado and New Mexico. By ensuring that real-time forecasts were still integrated into the National Weather Service Flood Warning System, the reports of about 200 citizen scientists contributed to what has been called the best mapped extreme rain event in Colorado history and possibly nationwide.
The Community Collaborative Rain, Hail and Snow (CoCoRaHS) Network played a pivotal role in this mapping. CoCoRaHS also shows how citizen science can help make data collection straightforward and inexpensive. To measure the impact and size of hail, for example, it uses a Styrofoam sheet covered with tin foil, creating a “hail pad” that has proven to be quite accurate.
The recognized value of citizen science is growing rapidly. NOAA has an app to crowdsource real-time precipitation data. If you feel a raindrop, or spot a snowflake, report it through NOAA’s mPING app. Precipitation reports have already topped 600,000, and the National Weather Service uses them to fine-tune forecasts…(More).”

Big Data Now

Curated on January 27, 2015August 15, 2018 by Stefaan Verhulst

Timothy McGovern at Radar – O’Reilly: “In the four years we’ve been producing Big Data Now, our wrap-up of important developments in the big data field, we’ve seen tools and applications mature, multiply, and coalesce into new categories. This year’s free wrap-up of Radar coverage is organized around seven themes:

Cognitive augmentation: As data processing and data analytics become more accessible, jobs that can be automated will go away. But to be clear, there are still many tasks where the combination of humans and machines produce superior results.
Intelligence matters: Artificial intelligence is now playing a bigger and bigger role in everyone’s lives, from sorting our email to rerouting our morning commutes, from detecting fraud in financial markets to predicting dangerous chemical spills. The computing power and algorithmic building blocks to put AI to work have never been more accessible.
The convergence of cheap sensors, fast networks, and distributed computation: The amount of quantified data available is increasing exponentially — and aside from tools for centrally handling huge volumes of time-series data as it arrives, devices and software are getting smarter about placing their own data accurately in context, extrapolating without needing to ‘check in’ constantly.
Reproducing, managing, and maintaining data pipelines: The coordination of processes and personnel within organizations to gather, store, analyze, and make use of data.
The evolving, maturing marketplace of big data components: Open-source components like Spark, Kafka, Cassandra, and ElasticSearch are reducing the need for companies to build in-house proprietary systems. On the other hand, vendors are developing industry-specific suites and applications optimized for the unique needs and data sources in a field.
The value of applying techniques from design and social science: While data science knows human behavior in the aggregate, design works in the particular, where A/B testing won’t apply — you only get one shot to communicate your proposal to a CEO, for example. Similarly, social science enables extrapolation from sparse data. Both sets of tools enable you to ask the right questions, and scope your problems and solutions realistically.
The importance of building a data culture: An organization that is comfortable with gathering data, curious about its significance, and willing to act on its results will perform demonstrably better than one that doesn’t. These priorities must be shared throughout the business.
The perils of big data: From poor analysis (driven by false correlation or lack of domain expertise) to intrusiveness (privacy invasion, price profiling, self-fulfilling predictions), big data has negative potential.