The Wisdom of Networks – and the Lessons of Wikipedia

Curated on Posted on by Stefaan Verhulst

Philip Reitinger at the Analogies Project: “Douglas Merrill said “All of us are smarter than any of us.”  This motto of crowdsourcing – looking to the information that can arise from the combined observation by and intelligence of many – is also the prescription for a more secure cyber future. Crowdsourcing security among machines – rather than people – is our best path forward.

Attackers have the advantage online for many reasons, including the ability to leverage a simple error into a significant compromise, to scale attacks more readily than defenses can scale, and to attack at a distance.  While the maxim that defenders have to be right all the time, while attackers only have to be right once, is not literally true, it conveys the dilemma of defenders.   The connectivity of our devices and agents is inexorably increasing, creating more targets for attack.  The complexity of the software we use and the network we must defend is also increasing, making an attack on the individual target or the network easier.  And the criticality of our connected systems to our lives is also growing and will continue to grow.  Together, this means that we live in a world of steadily increasing risk.

In this environment, the good guys and gals have one significant but counter-intuitive advantage:  the size of the network being defended. The soaring prevalence of smart devices is a risk only until it is not, until we combine the abilities of these devices to observe, to induce, and to act to defend the network itself.  The cyber ecosystem is the greatest sensor network imaginable, and the data generated by its sensors can drive collective intelligence and collective action to stem threats and isolate infections.  The ability of the network components to defend the network may make the future of cybersecurity on the Internet look very much like Wikipedia – one of the best known examples of crowdsourcing – with some obvious failures, but if of importance, generally quickly corrected….


What is necessary to enable the crowdsourcing of defense among network components?  A few years ago, while I was at the Department of Homeland Security, it published a paper entitled “Enabling Distributed Security in Cyberspace: Building a Healthy and Resilient Cyber Ecosystem with Automated Collective Action.” This paper posits three requirements:  

  • Automation so the network can act at Internet speed;
  • Interoperability so the barriers to effective collective (network or “crowd”) action are those we impose by policy, as opposed to those imposed on us by technology or process; and
  • Authentication to enhance the decision-making and action of the network against attacks.

It has been five years since the paper was published, and I still think these are the key elements of a more secure Internet future.  Until we enable the network to defend itself, using its own wisdom of crowds (of agents), offense wins.  People should do what people do best, adjust how the network defends itself, and take action when necessary based on intuition, rather than responding to alerts.  So when you think about future Internet security problems, think about Stephen Colbert and Wikipedia….(More)”

Next Generation Crowdsourcing for Collective Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by John Prpić : “New techniques leveraging IT-mediated crowds such as Crowdsensing, Situated Crowdsourcing, Spatial Crowdsourcing, and Wearables Crowdsourcing have now materially emerged. These techniques, here termed next generation Crowdsourcing, serve to extend Crowdsourcing efforts beyond the heretofore dominant desktop computing paradigm. Employing new configurations of hardware, software, and people, these techniques represent new forms of organization for IT-mediated crowds. However, it is not known how these new techniques change the processes and outcomes of IT-mediated crowds for Collective Intelligence purposes? The aim of this exploratory work is to begin to answer this question. The work ensues by outlining the relevant findings of the first generation Crowdsourcing paradigm, before reviewing the emerging literature pertaining to the new generation of Crowdsourcing techniques. Premised on this review, a collectively exhaustive and mutually exclusive typology is formed, organizing the next generation Crowdsourcing techniques along two salient dimensions common to all first generation Crowdsourcing techniques. As a result, this work situates the next generation Crowdsourcing techniques within the extant Crowdsourcing literature, and identifies new research avenues stemming directly from the analysis….(More)”

Cities, Data, and Digital Innovation

Curated on Posted on by Stefaan Verhulst

Paper by Mark Kleinman: “Developments in digital innovation and the availability of large-scale data sets create opportunities for new economic activities and new ways of delivering city services while raising concerns about privacy. This paper defines the terms Big Data, Open Data, Open Government, and Smart Cities and uses two case studies – London (U.K.) and Toronto – to examine questions about using data to drive economic growth, improve the accountability of government to citizens, and offer more digitally enabled services. The paper notes that London has been one of a handful of cities at the forefront of the Open Data movement and has been successful in developing its high-tech sector, although it has so far been less innovative in the use of “smart city” technology to improve services and lower costs. Toronto has also made efforts to harness data, although it is behind London in promoting Open Data. Moreover, although Toronto has many assets that could contribute to innovation and economic growth, including a growing high-technology sector, world-class universities and research base, and its role as a leading financial centre, it lacks a clear narrative about how these assets could be used to promote the city. The paper draws some general conclusions about the links between data innovation and economic growth, and between open data and open government, as well as ways to use big data and technological innovation to ensure greater efficiency in the provision of city services…(More)“

Crowdsourcing On-street Parking Space Detection

Curated on Posted on by Stefaan Verhulst

Paper by Ruizhi Liao et al in: “As the number of vehicles continues to grow, parking spaces are at a premium in city streets. Additionally, due to the lack of knowledge about street parking spaces, heuristic circling the blocks not only costs drivers’ time and fuel, but also increases city congestion. In the wake of recent trend to build convenient, green and energy-efficient smart cities, we rethink common techniques adopted by high-profile smart parking systems, and present a user-engaged (crowdsourcing) and sonar-based prototype to identify urban on-street parking spaces. The prototype includes an ultrasonic sensor, a GPS receiver and associated Arduino micro-controllers. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. Multiple road tests are conducted around Wheatley, Oxford to gather results and emulate the crowdsourcing approach. By extracting parked vehicles’ features from the collected trace, a supervised learning algorithm is developed to estimate roadside parking occupancy and spot illegal parking vehicles. A quantity estimation model is derived to calculate the required number of sensing units to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the crowdsourcing way would need substantially fewer sensors compared to the fixed sensing system…(More)”

Opportunities and Challenges of Policy Informatics: Tackling Complex Problems through the Combination of Open Data, Technology and Analytics

Curated on Posted on by Stefaan Verhulst

Gabriel Puron-Cid et al in the International Journal on Public Administration in the Digital Age: “Contemporary societies face complex problems that challenge the sustainability of their social and economic systems. Such problems may require joint efforts from the public and private sectors as well as from the society at large in order to find innovative solutions. In addition, the open government movement constitutes a revitalized wave of access to data to promote innovation through transparency, participation and collaboration. This paper argues that currently there is an opportunity to combine emergent information technologies, new analytical methods, and open data in order to develop innovative solutions to some of the pressing problems in modern societies. Therefore, the objective is to propose a conceptual model to better understand policy innovations based on three pillars: data, information technologies, and analytical methods and techniques. The potential benefits generated from the creation of organizations with advanced analytical capabilities within governments, universities, and non-governmental organizations are numerous and the expected positive impacts on society are significant. However, this paper also discusses some important political, organizational, and technical challenges…(More).

 

Value public information so we can trust it, rely on it and use it

Curated on Posted on by Stefaan Verhulst

Speech by David Fricker, the director general of the National Archives of Australia: “No-one can deny that we are in an age of information abundance. More and more we rely on information from a variety of sources and channels. Digital information is seductive, because it’s immediate, available and easy to move around. But digital information can be used for nefarious purposes. Social issues can be at odds with processes of government in this digital age. There is a tension between what is the information, where it comes from and how it’s going to be used.

How do we know if the information has reached us without being changed, whether that’s intentional or not?

How do we know that government digital information will be the authoritative source when the pace of information exchange is so rapid? In short, how do we know what to trust?

“It’s everyone’s responsibly to contribute to a transparent government, and that means changes in our thinking and in our actions.”

Consider the challenges and risks that come with the digital age: what does it really mean to have transparency and integrity of government in today’s digital environment?…

What does the digital age mean for government? Government should be delivering services online, which means thinking about location, timeliness and information accessibility. It’s about getting public-sector data out there, into the public, making it available to fuel the digital economy. And it’s about a process of change across government to make sure that we’re breaking down all of those silos, and the duplication and fragmentation which exist across government agencies in the application of information, communications, and technology…..

The digital age is about the digital economy, it’s about rethinking the economy of the nation through the lens of information that enables it. It’s understanding that a nation will be enriched, in terms of culture life, prosperity and rights, if we embrace the digital economy. And that’s a weighty responsibility. But the responsibility is not mine alone. It’s a responsibility of everyone in the government who makes records in their daily work. It’s everyone’s responsibly to contribute to a transparent government. And that means changes in our thinking and in our actions….

What has changed about democracy in the digital age? Once upon a time if you wanted to express your anger about something, you might write a letter to the editor of the paper, to the government department, or to your local member and then expect some sort of an argument or discussion as a response. Now, you can bypass all of that. You might post an inflammatory tweet or blog, your comment gathers momentum, you pick the right hashtag, and off we go. It’s all happening: you’re trending on Twitter…..

If I turn to transparency now, at the top of the list is the basic recognition that government information is public information. The information of the government belongs to the people who elected that government. It’s a fundamental of democratic values. It also means that there’s got to be more public participation in the development of public policy, which means if you’re going to have evidence-based, informed, policy development; government information has to be available, anywhere, anytime….

Good information governance is at the heart of managing digital information to provide access to that information into the future — ready access to government information is vital for transparency. Only when information is digital and managed well can government share it effectively with the Australian community, to the benefit of society and the economy.

There are many examples where poor information management, or poor information governance, has led to failures — both in the private and public sectors. Professor Peter Shergold’s recent report, Learning from Failure, why large government policy initiatives have gone so badly wrong in the past and how the chances of success in the future can be improved, highlights examples such as the Home Insulation Program, the NBN and Building the Education Revolution….(Full Speech)

Ebola: A Big Data Disaster

Curated on Posted on by Stefaan Verhulst

Study by Sean Martin McDonald: “…undertaken with support from the Open Society Foundation, Ford Foundation, and Media Democracy Fund, explores the use of Big Data in the form of Call Detail Record (CDR) data in humanitarian crisis.

It discusses the challenges of digital humanitarian coordination in health emergencies like the Ebola outbreak in West Africa, and the marked tension in the debate around experimentation with humanitarian technologies and the impact on privacy. McDonald’s research focuses on the two primary legal and human rights frameworks, privacy and property, to question the impact of unregulated use of CDR’s on human rights. It also highlights how the diffusion of data science to the realm of international development constitutes a genuine opportunity to bring powerful new tools to fight crisis and emergencies.

Analysing the risks of using CDRs to perform migration analysis and contact tracing without user consent, as well as the application of big data to disease surveillance is an important entry point into the debate around use of Big Data for development and humanitarian aid. The paper also raises crucial questions of legal significance about the access to information, the limitation of data sharing, and the concept of proportionality in privacy invasion in the public good. These issues hold great relevance in today’s time where big data and its emerging role for development, involving its actual and potential uses as well as harms is under consideration across the world.

The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modelling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights….

See Sean Martin McDonald – “Ebola: A Big Data Disaster” (PDF).

 

A machine intelligence commission for the UK

Curated on Posted on by Stefaan Verhulst

Geoff Mulgan at NESTA: ” This paper makes the case for creating a Machine Intelligence Commission – a new public institution to help the development of new generations of algorithms, machine learning tools and uses of big data, ensuring that the public interest is protected.

I argue that new institutions of this kind – which can interrogate, inspect and influence technological development – are a precondition for growing informed public trust. That trust will, in turn, be essential if we are to reap the full potential public and economic benefits from new technologies. The proposal draws on lessons from fields such as human fertilisation, biotech and energy, which have shown how trust can be earned, and how new industries can be grown.  It also draws on lessons from the mistakes made in fields like GM crops and personal health data, where lack of trust has impeded progress….(More)”

The impact of a move towards Open Data in West Africa

Curated on Posted on by Stefaan Verhulst

 at the Georgetown Journal of International Affairs:  “The concept of “open data” is not new, but its definition is quite recent. Since computers began communicating through networks, engineers have been developing standards to share data. The open data philosophy holds that some data should be freely available for use, reuse, distribute and publish without copyright and patent controls. Several mechanisms can also limit access to data like restricted database access, use of proprietary technologies or encryption. Ultimately, open data buttresses government initiatives to boost innovation, support transparency, empower citizens, encourage accountability, and fight corruption.

West Africa is primed for open data. The region experienced a 6% growth in 2014, according to the Africa Development Bank. Its Internet user network is also growing: 17% of the sub-Saharan population owned a unique smartphone in 2013, a number projected to grow to 37% by 2020 according to the GSMA. To improve the quality of governance and services in the digital age, the region must develop new infrastructures, revise digital strategies, simplify procurement procedures, adapt legal frameworks, and allow access to public data. Open data can enhance local economies and the standard of living.

This paper speaks towards the impact of open data in West Africa. First it assesses open data as a positive tool for governance and civil society. Then, it analyzes the current situation of open data across the region. Finally, it highlights specific best practices for enhancing impact in the future….(More)”

Public-Private Partnerships for Statistics: Lessons Learned, Future Steps

Curated on Posted on by Stefaan Verhulst

Report by Nicholas Robin, Thilo Klein and Johannes Jütting for Paris 21: “Non-offcial sources of data, big data in particular, are currently attracting enormous interest in the world of official statistics. An impressive body of work focuses on how different types of big data (telecom data, social media, sensors, etc.) can be used to fll specifc data gaps, especially with regard to the post-2015 agenda and the associated technology challenges. The focus of this paper is on a different aspect, but one that is of crucial importance: what are the perspectives of the commercial operations and national statistical offces which respectively produce and might use this data and which incentives, business models and protocols are needed in order to leverage non-offcial data sources within the offcial statistics community?

Public-private partnerships (PPPs) offer signifcant opportunities such as cost effectiveness, timeliness, granularity, new indicators, but also present a range of challenges that need to be surmounted. These comprise technical diffculties, risks related to data confdentiality as well as a lack of incentives. Nevertheless, a number of collaborative projects have already emerged and can be

Nevertheless, a number of collaborative projects have already emerged and can be classified into four ideal types: namely the in-house production of statistics by the data provider, the transfer of private data sets to the end user, the transfer of private data sets to a trusted third party for processing and/or analysis, and the outsourcing of national statistical office functions (the only model which is not centred around a data-sharing dimension). In developing countries, a severe lack of resources and particular statistical needs (to adopt a system-wide approach within national statistical systems and fill statistical gaps which are relevant to national development plans) highlight the importance of harnessing the private sector’s resources and point to the most holistic models (in-house and third party) in which the private sector contributes to the processing and analysis of data. The following key lessons are drawn from four case studies….(More)”